Inspeção de estruturas de pontes e túneis baseada em VANTs: uma revisão das aplicações

Toriumi, Fabio Yukio; Bittencourt, Túlio Nogueira; Futai, Marcos Massao

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REVIEW • Rev. IBRACON Estrut. Mater. 16 (1) • 2023 • https://doi.org/10.1590/S1983-41952023000100003 copiar

Inspeção de estruturas de pontes e túneis baseada em VANTs: uma revisão das aplicações

Autoria SCIMAGO INSTITUTIONS RANKINGS

resumo:

Pontes e túneis são estruturas complexas e largas que exigem inspeções periódicas para avaliar suas condições físicas. Embora ambos possuam projetos e construções diferentes entre si, um problema comum que eles compartilham são as desvantagens que suas inspeções convencionais enfrentam. Além disso, procedimentos convencionais não só são trabalhosos, demorados e dispendiosos, mas também envolvem lugares altos e/ou de difícil acesso, muitas vezes expondo os inspetores especializados a perigos. Para superar estes problemas, o Veículo Aéreo Não Tripulado (VANT) está sendo explorado para automatizar essas inspeções. Recentemente, o número de pesquisas que o empregam na avaliação da condição de infraestruturas civis vem crescendo nos últimos anos, especialmente para a inspeção de estruturas complexas e largas. Ao contrário da inspeção de pontes com VANT que já possui alguns artigos de revisão disponíveis na literatura, não há nenhum ainda para a inspeção de túneis com VANT, até onde os autores saibam. Portanto, este artigo pretende conduzir não somente uma revisão das poucas pesquisas disponíveis na literatura sobre inspeção de túneis com VANT, como também uma revisão atualizada das pesquisas sobre inspeção de pontes com VANT. Finalmente, os principais desafios e as tendências futuras da inspeção com VANT dessas duas estruturas são discutidas, seguidas das conclusões da revisão.

Palavras-chave:
veículo aéreo não tripulado; ponte; túnel; inspeção; aplicações

Ref.	Custom-built UAV		Application scale	Findings
Ref.	(platform)	(payload)	Application scale	Findings
[¹⁵15 T. Özaslan, S. Shen, Y. Mulgaonkar, N. Michael, and V. Kumar, “Inspection of penstocks and featureless tunnel-like environments using micro UAVs,” in Field and Service Robotics, L. Mejias, P. Corke, and J. Roberts, Eds., Cham: Springer International Publishing, 2015, pp. 123–136, http://dx.doi.org/10.1007/978-3-319-07488-7_9. http://dx.doi.org/10.1007/978-3-319-0748... ]	4-rotor AscTec Pelican	· 1 Hokuyo LiDAR; · LED lights; · 1 inertial measurement unit (IMU); · 1 1.6 GHz Atom Intel processor.	· published in 2015, was the first literature study (to their knowledge) that focused on UAV localization and autonomous control in featureless three-dimensional (3D) tunnel-like environments; · performed field experiments in three different sites: § in penstocks of both Carter and Allatoona Dams, in Georgia § in a long building hallway at the University of Pennsylvania.	· although a camera was not used in this research, the experimental results showed the feasibility of the UAV-based inspection of tunnel-like environments, making it a reasonable choice for future inspections by equipping the UAV with onboard cameras; · the proposed approach can be used for other tunnels, as those in transportation networks, besides the penstocks in dams and hydroelectric power plants that were explored in the experiments.
[¹⁶16 T. Özaslan et al., "Towards fully autonomous visual inspection of dark featureless dam penstocks using MAVs,” in 2016 IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), Daejeon, Korea, 2016, pp. 4998–5005.]	6-rotor KMel Robotics KHex	· 2 Hokuyo UST-20LX 2D LiDARs; · 4 VGA resolution BlueFox cameras; · 8 10W Cree power-LEDs; · 1 IMU; · 1 Intel i7 NUC board; · other sensors.	· proposed a complete system design to collect detailed images of dark, featureless, symmetric and GPS-denied environments (as inside penstocks and tunnels) for their inspection using fully autonomous MAV; · performed field experiments in penstocks of both Carters and Glen Canyon Dams, from which a 360º panoramic image reconstruction and its 3D visualization were obtained.	· the 360º panoramic image reconstruction and its 3D visualization aid for a convenient inspection of inner penstock and tunnel environments; · a safe autonomy was achieved in dark, featureless, symmetric and GPS-denied environment by fusing the data obtained from the four VGA resolution cameras and the two LiDARs.
[¹⁷17 T. Özaslan et al., "Autonomous navigation and mapping for inspection of penstocks and tunnels with MAVs," IEEE Robot. Autom. Lett., vol. 2, no. 3, pp. 1740–1747, 2017, http://dx.doi.org/10.1109/LRA.2017.2699790. http://dx.doi.org/10.1109/LRA.2017.26997... ]	6-rotor DJI-F550	· 1 Velodyne Puck LITE LiDAR; · 4 Chameleon3 cameras (each rectified with four Cree XHP-50 high-power LEDs); · 1 PixHawk onboard controller (with IMU); · 1 Intel i7 NUC board.	· presented a new approach to achieve autonomous UAV-based inspection of tunnels and penstocks by addressing the navigation, mapping, estimation, and control problems; · performed field experiments in a penstock of Center Hill Dam, where the autonomous and shared control navigation, environment mapping, and state estimation are demonstrated.	· the autonomous navigation was executed by the onboard controller that receives feedback from the proposed estimator while allowing the shared control navigation with an operator even when the UAV is out of his sight, provided by a visual interface that shows him the UAV sensory data in real-time.
[¹⁸18 T. Iwamoto, T. Enaka, and K. Tada, "Development of testing machine for tunnel inspection using multi-rotor UAV," J. Phys. Conf. Ser., vol. 842, no. 1, pp. 012068, 2017, http://dx.doi.org/10.1088/1742-6596/842/1/012068. http://dx.doi.org/10.1088/1742-6596/842/... ]	4-rotor DJI-F330	· measurement component (hammering device and microphone); · traversing component (Zumo Robot platform for Arduino); · control component (wireless communication device, DJI Naza-M Lite flight controller, and a microcomputer); · positioning system (camera).	· developed an onboard hammering device for their custom-built UAV to perform tunnel inspection with the impact acoustic method, where the UAV thrust component push it along a concrete wall and the traversing component enable its movement across the structure, while the impact sounds made by the onboard hammering device are acquired by a microphone for later analysis.	· the proposed mechanism was tested in inclined composite panels, achieving free movement in an angle of inclination up to 15º, and its inspection performance was evaluated with experimental results, where the peak frequency of the impact sound (collected by hitting concrete pieces during flight) was successfully detected, validating the proposed mechanism.
[¹⁹19 C. H. Vong, R. Ravitharan, P. Reichl, J. Chevin, and H. Chung, “Small scale unmanned aerial system (UAS) for railway culvert and tunnel inspection,” in ICRT 2017: Railway Develop., Operations, Maint., Reston, VA, 2018, pp. 1024–1032, http://dx.doi.org/10.1061/9780784481257.102. http://dx.doi.org/10.1061/9780784481257.... ]	4-rotor custom frame	· 1 LiDAR; · onboard computers; · other components (not specified).	· aimed to develop a railway culvert inspection tool, composed of the custom-built UAV and the developed control algorithm for semi-autonomous navigation and autonomous alignment of the UAV centroid with the culvert cross section center; · performed field experiments in a built 2.4m long small tunnel with an inner rectangular cross section of 1.38m x 1.5m, where the UAV achieved self-stabilization.	· despite some portion of the LiDAR field of view being obstructed by the UAV frame (as it is positioned in the middle of the frame), it had insignificant impact on the overall system performance; · an adjustment on the UAV flight altitude was required during the experiments to avoid the ground effect (an air cushion generated below the UAV), originated while hovering close to the ground, which was aggravated by the confined space and disturbed the UAV stable flight.
[²⁰20 T. Hongyu, “Inspection of human inaccessible tunnel using unmanned aerial vehicle,” M.S. thesis, Dept. Elect. Comput. Eng., Nat. Univ. Singap., Singapore, 2018.]	4-rotor custom collision tolerant frame	· 1 Hokuyo UST-20LX 2D LiDAR; · 1 Kodak PIXPRO SP360 4K omnidirectional camera; · 1 high power LED; · 1 StereoLab ZED stereo camera; · 6 TeraRanger One range sensors; · other components.	· attempted to address the inspection problem of human inaccessible tunnels with UAV autonomous navigation and collection of visual data inside these environments; · performed both simulations in gazebo and field experiments in a hydraulic tunnel.	· the autonomous navigation was achieved by using the stereo camera, even when losing communication over long tunnel stretches; · the omnidirectional camera with ultra-wide view angle was able to capture visual image of the inner tunnel wall condition, even inside dark environments.
[²¹21 C. H. Tan et al., "A smart unmanned aerial vehicle (UAV) based imaging system for inspection of deep hazardous tunnels," Water Pract. Technol., vol. 13, no. 4, pp. 991–1000, 2018, http://dx.doi.org/10.2166/wpt.2018.105. http://dx.doi.org/10.2166/wpt.2018.105... ]	4-rotor custom frame	· 1 developed rotating camera system (integrated with high luminance LEDs); · 6 TeraRanger One range sensors; · 1 LightWare SF11/C laser altimeter (down-pointing); · 1 Pixhawk 2.1 flight controller (with IMU); · 1 Intel Edison (as a companion computer); · other sensors.	· designed and developed a novel UAV (called “SWIRL” - Surveyor With Intelligent Rotating Lens) and imaging system for deep tunnel network inspection, where a novel rotating camera system perform spiral panoramic imaging (tested and validated in a field experiment at Connaught Drive underpass, Singapore); · performed both simulations using Gazebo and two field experiments to evaluate the UAV autonomous flight: § in a covered section at Eu Tong Sen Canal (to evaluate the horizontal traveling through a poor illuminated tunnel); § in an entry shaft of the Deep Tunnel Sewerage System in Singapore (to evaluate the vertical traveling in shafts).	· the limited data required by the proposed location method resulted in a relative lower UAV payload and power consumption, yielding to more than 35min of autonomous flight time; · during the field experiment in the entry shaft, its constant high velocity updraft, together with the turbulence generated by its enclosed environment, acted negatively on the UAV flight performance, rising its positioning error.
[²²22 R. S. Pahwa, K. Y. Chan, J. Bai, V. B. Saputra, M. N. Do, and S. Foong, “Dense 3D reconstruction for visual tunnel inspection using unmanned aerial vehicle,” in 2019 IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), Macau, China, 2019, pp. 7025–7032.]	4-rotor custom frame	· 1 GoPro Hero4 camera (mounted on a rotating system with light source); · other components (not specified).	· proposed a method that exploits Bundle Adjustment, Structure-from-Motion and available geometry priors to robustly estimate the GoPro Hero4 camera pose, which is rotated 360º around the shaft of the custom-built UAV, to take a sequence of images while the UAV traverses tunnel-like environments, in order to perform panoramic cylindrical views and fully-dense 3D reconstructions; · performed field experiments in an underpass, where the rotating camera system was mounted on a tripod stand and three different datasets were collected to evaluate the proposed method, whose results were compared with the Multi-View Reconstruction Environment method.	· the proposed method allowed the use of as few images as possible to reconstruct a fully-dense 3D scene by using the prior knowledge of local geometry (e.g., the tunnel cross section diameter), enabling this way a pre-configuration of the UAV speed for an efficient image capture.
[²³23 S. S. Mansouri, C. Kanellakis, D. Kominiak, and G. Nikolakopoulos, "Deploying MAVs for autonomous navigation in dark underground mine environments," Robot. Auton. Syst., vol. 126, pp. 103472, 2020, http://dx.doi.org/10.1016/j.robot.2020.103472. http://dx.doi.org/10.1016/j.robot.2020.1... ]	4-rotor custom frame	· 1 2D rotating RPLIDAR (range measurement); · 1 LIDAR-lite v3 (height measurement); · 1 GoPro Hero7 camera; · 2 10W LED light bars; · 1 PX4Flow optical flow sensor (velocity estimation); · 1 Aaeon UP-Board processing unit (with an Intel Atom x5-Z8350 processor, and ROSflight controller).	· used a ROSflight based custom-built MAV (developed by Luleå University of Technology) to rapidly explore unknown areas while providing feedback to the operator, for which a framework focusing on MAV navigation, control and vision is proposed; · performed field experiments in a dark underground mine in Sweden, located at 790m deep, where three different test cases (in a 150m long tunnel area with a cross section of 6m x 4m) evaluated the feasibility and adaptability of the proposed framework.	· the proposed framework allowed flexibility and modularity on the MAV system by proposing two different approaches for its navigation (one depending on the 2D LiDAR measurements, and the other on the camera image streams), which accomplished the autonomous flight, and by assigning different sensors to the state estimation; · the field experiments demonstrated the capability of low-cost and resource-constrained MAVs to navigate in tunnel confined environments.
[²⁴24 C. Feng, H. Zhang, Y. Li, S. Wang, and H. Wang, "Efficient real-time defect detection for spillway tunnel using deep learning," J. Real-Time Image Process., vol. 18, no. 6, pp. 1–11, 2021, http://dx.doi.org/10.1007/s11554-021-01130-x. http://dx.doi.org/10.1007/s11554-021-011... ]	not specified	· 1 high definition camera; · 1 fill light; · other components (not specified).	· presented a real-time defect detection (focused on exposed rebar) of spillway tunnels using UAV and deep learning; · performed field experiments in a spillway tunnel of a hydro-power station in the Minjiang River, where the UAV collected images within a specified area, whose siltation, stagnant water, and light conditions yielded to challenges for image collection.	· the proposed deep learning method allowed a relative reduction of its model size and parameter numbers, yielding to a better real-time performance, which is a key feature for future onboard UAV application.

Model (type)	Cameras (resolution)	Flight time (approx.)	References
SenseFly Albris (4-rotors)	Int.* (38MP /1280x720 video); IR (80x60 video).	up to 22min	[⁴³43 J. Wells and B. Lovelace, Improving the Quality of Bridge Inspections Using Unmanned Aircraft Systems (UAS) (Tech. Rep. MN/RC 2018-26). St. Paul, MN: Minnesota Dept. Transp., 2018. [Online]. Available: https://trid.trb.org/view/1539868 https://trid.trb.org/view/1539868... ]–[⁴⁵45 M. N. Gillins, “Unmanned aircraft systems for bridge inspection: testing and developing end-to-end operational workflow,” M.S. thesis, Oregon State Univ., Corvallis, OR, USA, 2016. [Online]. Available: http://hdl.handle.net/1957/60001 http://hdl.handle.net/1957/60001... ]
3DR Iris (4-rotors)	GoPro Hero 3 or 4 Series (5~12MP/ 1080p~4K video).	10~13min	[⁴⁶46 S. Dorafshan, R. J. Thomas, C. Coopmans, and M. Maguire, “Deep learning neural networks for sUAS-assisted structural inspections: feasibility and application,” in 2018 Int. Conf. Unmanned Aircr. Syst. (ICUAS), Dallas, TX, USA, 2018, pp. 874–882.]–[⁴⁹49 S. Dorafshan, R. J. Thomas, and M. Maguire, "Fatigue crack detection using unmanned aerial systems in fracture critical inspection of steel bridges," J. Bridge Eng., vol. 23, no. 10, pp. 04018078, 2018, http://dx.doi.org/10.1061/(ASCE)BE.1943-5592.0001291. http://dx.doi.org/10.1061/(ASCE)BE.1943-... ]
DJI Mavic Series (4-rotors)	Int. (12~48MP/ 1080p~4K video).	21~31min	[⁴⁶46 S. Dorafshan, R. J. Thomas, C. Coopmans, and M. Maguire, “Deep learning neural networks for sUAS-assisted structural inspections: feasibility and application,” in 2018 Int. Conf. Unmanned Aircr. Syst. (ICUAS), Dallas, TX, USA, 2018, pp. 874–882.]–[⁵⁶56 B. J. Perry, Y. Guo, R. Atadero, and J. W. van de Lindt, "Tracking bridge condition over time using recurrent UAV-based inspection,” in Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations. CRC Press, 2021, pp. 286–291.]
DJI Phantom 3 Series (4-rotors)	Int. (12~12.4MP/ 1080p~4K video).	23~25min	[⁴⁵45 M. N. Gillins, “Unmanned aircraft systems for bridge inspection: testing and developing end-to-end operational workflow,” M.S. thesis, Oregon State Univ., Corvallis, OR, USA, 2016. [Online]. Available: http://hdl.handle.net/1957/60001 http://hdl.handle.net/1957/60001... ], [⁵⁷57 S. Hiasa, E. Karaaslan, W. Shattenkirk, C. Mildner, and F. N. Catbas, “Bridge inspection and condition assessment using image-based technologies with UAVs,” in Struct. Congr. 2018: Bridges, Transp. Struct., Nonbuild. Struct., Fort Worth, TX, USA, 2018, pp. 217–228.]–[⁵⁹59 H. Yoon, J. Shin, and B. F. Spencer Jr, "Structural displacement measurement using an unmanned aerial system," Comput. Aided Civ. Infrastruct. Eng., vol. 33, no. 3, pp. 183–192, 2018, http://dx.doi.org/10.1111/mice.12338. http://dx.doi.org/10.1111/mice.12338... ]
DJI Phantom 4 Series (4-rotors)	Int. (20MP/ 4K video).	27~30min	[¹¹11 J. Seo, L. Duque, and J. Wacker, “Glued-laminated timber arch bridge inspection using UAV,” in Comput. Civ. Eng. 2019: Smart Cities, Sustainability, and Resilience, Reston, 2019, pp. 336–342.], [⁵⁰50 B. J. Perry, “Streamlined bridge inspection framework utilizing unmanned aerial vehicles (UAVs),” M.S. thesis, Dept. Civ. Environ. Eng., Colorado State Univ., Fort Collins, CO, USA, 2019. [Online]. Available: https://hdl.handle.net/10217/197417 https://hdl.handle.net/10217/197417... ], [⁵¹51 B. J. Perry, Y. Guo, R. Atadero, and J. W. van de Lindt, "Streamlined bridge inspection system utilizing unmanned aerial vehicles (UAVs) and machine learning," Meas., vol. 164, pp. 108048, 2020, http://dx.doi.org/10.1016/j.measurement.2020.108048. http://dx.doi.org/10.1016/j.measurement.... ], [⁵⁵55 B. J. Perry, Y. Guo, R. Atadero, and J. W. van de Lindt, "Unmanned aerial vehicle (UAV)-enabled bridge inspection framework,” in Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations, H. Yokota and D.M. Frangopol, Eds., London: CRC Press, 2021, pp. 158–165.], [⁶⁰60 J. J. Lin, A. Ibrahim, S. Sarwade, and M. Golparvar-Fard, "Bridge inspection with aerial robots: Automating the entire pipeline of visual data capture, 3D mapping, defect detection, analysis, and reporting," J. Comput. Civ. Eng., vol. 35, no. 2, pp. 04020064, 2021, http://dx.doi.org/10.1061/(ASCE)CP.1943-5487.0000954. http://dx.doi.org/10.1061/(ASCE)CP.1943-... ]–[⁷⁰70 E. Ciampa, L. D. Vito, and M. R. Pecce, "Practical issues on the use of drones for construction inspections," J. Phys. Conf. Ser., vol. 1249, no. 1, pp. 012016, May 2019, http://dx.doi.org/10.1088/1742-6596/1249/1/012016. http://dx.doi.org/10.1088/1742-6596/1249... ]
DJI Inspire Series (4-rotors)	Zenmuse X Series (12.4~24MP/ 4K video); IR - FLIR Vue Pro (640x512 video).	18~27min	[⁷¹71 I.-H. Kim, H. Jeon, S.-C. Baek, W.-H. Hong, and H.-J. Jung, "Application of crack identification techniques for an aging concrete bridge inspection using an unmanned aerial vehicle," Sens., vol. 18, no. 6, pp. 1881, 2018, http://dx.doi.org/10.3390/s18061881. http://dx.doi.org/10.3390/s18061881... ]–[⁷⁵75 T. Omar and M. L. Nehdi, "Thermal detection of subsurface delaminations in reinforced concrete bridge decks using unmanned aerial vehicle," Spec. Publ., vol. 331, pp. 1–14, 2019.]
DJI Matrice 100 Series (4-rotors)	Zenmuse X Series or Zenmuse Z3 (3.5x Optical Zoom).	19~40min	[⁷⁶76 Y. Z. Ayele, M. Aliyari, D. Griffiths, and E. L. Droguett, "Automatic crack segmentation for UAV-assisted bridge inspection," Energies, vol. 13, no. 23, pp. 6250, 2020, http://dx.doi.org/10.3390/en13236250. http://dx.doi.org/10.3390/en13236250... ], [⁷⁷77 M. Aliyari, B. Ashrafi, and Y. Z. Ayele, "Hazards identification and risk assessment for UAV-assisted bridge inspections," Struct. Infrastruct. Eng., vol. 18, no. 3, pp. 412–428, 2022, http://dx.doi.org/10.1080/15732479.2020.1858878. http://dx.doi.org/10.1080/15732479.2020.... ]
DJI Matrice 200 Series (4-rotors)	Zenmuse X Series or Zenmuse Z30 (30x Optical Zoom).	13~38min	[⁷⁸78 Z. Yu, Y. Shen, and C. Shen, "A real-time detection approach for bridge cracks based on YOLOv4-FPM," Autom. Construct., vol. 122, pp. 103514, 2021, http://dx.doi.org/10.1016/j.autcon.2020.103514. http://dx.doi.org/10.1016/j.autcon.2020.... ]–[⁸²82 V. H. Mac, Q. H. Tran, J. Huh, N. S. Doan, C. Kang, and D. Han, "Detection of delamination with various width-to-depth ratios in concrete bridge deck using passive IRT: Limits and applicability," Mater., vol. 12, no. 23, pp. 3996, 2019, http://dx.doi.org/10.3390/ma12233996. http://dx.doi.org/10.3390/ma12233996... ]
DJI Matrice 600 Series (6-rotors)	Zenmuse X Series; Fujifilm GFX 50S (51.4MP/ 1080p video).	16~40min	[⁵⁰50 B. J. Perry, “Streamlined bridge inspection framework utilizing unmanned aerial vehicles (UAVs),” M.S. thesis, Dept. Civ. Environ. Eng., Colorado State Univ., Fort Collins, CO, USA, 2019. [Online]. Available: https://hdl.handle.net/10217/197417 https://hdl.handle.net/10217/197417... ], [⁵¹51 B. J. Perry, Y. Guo, R. Atadero, and J. W. van de Lindt, "Streamlined bridge inspection system utilizing unmanned aerial vehicles (UAVs) and machine learning," Meas., vol. 164, pp. 108048, 2020, http://dx.doi.org/10.1016/j.measurement.2020.108048. http://dx.doi.org/10.1016/j.measurement.... ], [⁵⁵55 B. J. Perry, Y. Guo, R. Atadero, and J. W. van de Lindt, "Unmanned aerial vehicle (UAV)-enabled bridge inspection framework,” in Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations, H. Yokota and D.M. Frangopol, Eds., London: CRC Press, 2021, pp. 158–165.], [⁸³83 D. Erdenebat and D. Waldmann, "Application of the DAD method for damage localisation on an existing bridge structure using close-range UAV photogrammetry," Eng. Struct., vol. 218, pp. 110727, 2020, http://dx.doi.org/10.1016/j.engstruct.2020.110727. http://dx.doi.org/10.1016/j.engstruct.20... ]–[⁸⁷87 C. Cheng, Z. Shang, and Z. Shen, "Automatic delamination segmentation for bridge deck based on encoder-decoder deep learning through UAV-based thermography," NDT Int., vol. 116, pp. 102341, 2020, http://dx.doi.org/10.1016/j.ndteint.2020.102341. http://dx.doi.org/10.1016/j.ndteint.2020... ]
Bergen Hexacopter (6-rotors)	Nikon D800 (36.3MP/ 1080p video); IR - FLIR Tau 2 (640x512 video).	18~30min	[⁸8 C. Brooks et al., Evaluating the Use of Unmanned Aerial Vehicles for Transportation Purposes (Tech. Rep. RC-1616). Lansing, MI: Michigan Dept. Transp., 2014, Accessed: Mar. 30, 2021. [Online]. Available: https://trid.trb.org/view/1352503 https://trid.trb.org/view/1352503... ], [⁸⁸88 R. Escobar-Wolf, T. Oommen, C. N. Brooks, R. J. Dobson, and T. M. Ahlborn, "Unmanned aerial vehicle (UAV)-based assessment of concrete bridge deck delamination using thermal and visible camera sensors: a preliminary analysis," Res. Nondestruct. Eval., vol. 29, no. 4, pp. 183–198, 2018, http://dx.doi.org/10.1080/09349847.2017.1304597. http://dx.doi.org/10.1080/09349847.2017.... ], [⁸⁹89 F. Jalinoos, M. Amjadian, A. K. Agrawal, C. Brooks, and D. Banach, "Experimental evaluation of unmanned aerial system for measuring bridge movement," J. Bridge Eng., vol. 25, no. 1, pp. 04019132, 2020, http://dx.doi.org/10.1061/(ASCE)BE.1943-5592.0001508. http://dx.doi.org/10.1061/(ASCE)BE.1943-... ]
Intel/AscTec Falcon 8 (8-rotors)	Sony NEX-5 (14.2MP/ 1080i video); Sony Alpha 7R (36.4MP/ 1080p video); Panasonic Lumix TZ22 (14.1MP/ 1080i video).	12~20min	[⁹9 N. Hallermann and G. Morgenthal, “Visual inspection strategies for large bridges using unmanned aerial vehicles (UAV),” in Proc. 7th Int. Conf. Bridge Maint., Saf. Manage. (IABMAS), Shanghai, China, 2014, pp. 661–667.], [⁹⁰90 G. Morgenthal et al., "Framework for automated UAS-based structural condition assessment of bridges," Autom. Construct., vol. 97, pp. 77–95, 2019, http://dx.doi.org/10.1016/j.autcon.2018.10.006. http://dx.doi.org/10.1016/j.autcon.2018.... ], [⁹¹91 K. W. Lee and J. K. Park, "Modeling and management of bridge structures using unmanned aerial vehicle in korea," Sens. Mater., vol. 31, no. 11, pp. 3765–3772, 2019, http://dx.doi.org/10.18494/SAM.2019.2467. http://dx.doi.org/10.18494/SAM.2019.2467... ]
Other COTS	--------------	--------------	[⁹²92 D. Reagan, A. Sabato, and C. Niezrecki, "Feasibility of using digital image correlation for unmanned aerial vehicle structural health monitoring of bridges," Struct. Health Monit., vol. 17, no. 5, pp. 1056–1072, 2018, http://dx.doi.org/10.1177/1475921717735326. http://dx.doi.org/10.1177/14759217177353... ]–[¹⁰⁴104 Q. Zhu, T. H. Dinh, M. D. Phung, and Q. P. Ha, “Hierarchical convolutional neural network with feature preservation and autotuned thresholding for crack detection,” IEEE Access, vol. 9, pp. 60201–60214, 2021.]
Custom-built	--------------	--------------	[¹⁰10 L. D. Otero, N. Gagliardo, D. Dalli, W.-H. Huang, and P. Cosentino, Proof of Concept for Using Unmanned Aerial Vehicles for High Mast Pole and Bridge Inspections (Tech. Rep. BDV28-TWO-977-02). Tallahassee, FL: Florida Dept. Transp., Res. Center, 2015, http://dx.doi.org/10.13140/RG.2.1.4567.2166. http://dx.doi.org/10.13140/RG.2.1.4567.2... ], [⁴⁷47 S. Dorafshan, M. Maguire, N. V. Hoffer, and C. Coopmans, "Challenges in bridge inspection using small unmanned aerial systems: results and lessons learned,” in 2017 Int. Conf. Unmanned Aircr. Syst. (ICUAS), Miami, FL, USA, 2017, pp. 1722–1730.]–[⁴⁹49 S. Dorafshan, R. J. Thomas, and M. Maguire, "Fatigue crack detection using unmanned aerial systems in fracture critical inspection of steel bridges," J. Bridge Eng., vol. 23, no. 10, pp. 04018078, 2018, http://dx.doi.org/10.1061/(ASCE)BE.1943-5592.0001291. http://dx.doi.org/10.1061/(ASCE)BE.1943-... ], [¹⁰⁵105 L. Yang, B. Li, W. Li, H. Brand, B. Jiang, and J. Xiao, "Concrete defects inspection and 3D mapping using CityFlyer quadrotor robot,” IEEE/CAA J," Autom. Sin., vol. 7, no. 4, pp. 991–1002, 2020, http://dx.doi.org/10.1109/JAS.2020.1003234. http://dx.doi.org/10.1109/JAS.2020.10032... ]–[¹³¹131 X. Peng, X. Zhong, C. Zhao, A. Chen, and T. Zhang, "A UAV-based machine vision method for bridge crack recognition and width quantification through hybrid feature learning," Constr. Build. Mater., vol. 299, pp. 123896, 2021, http://dx.doi.org/10.1016/j.conbuildmat.2021.123896. http://dx.doi.org/10.1016/j.conbuildmat.... ]
Not specified	--------------	--------------	[¹³²132 H.-J. Jung, J.-H. Lee, S. Yoon, and I.-H. Kim, "Bridge inspection and condition assessment using unmanned aerial vehicles (UAVs): major challenges and solutions from a practical perspective," Smart Struct. Syst., vol. 24, no. 5, pp. 669–681, 2019, http://dx.doi.org/10.12989/sss.2019.24.5.669. http://dx.doi.org/10.12989/sss.2019.24.5... ]–[¹⁵¹151 J. Shi, J. Dang, and R. Zuo, "Bridge damage cropping-and-stitching segmentation using fully convolutional network based on images from UAVs,” in Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations. CRC Press, 2021, pp. 264–270.]

Structure material	Defect type	Inspection type
Structure material	Defect type	(manual)	(automatic)
Concrete	Crack	[¹⁰10 L. D. Otero, N. Gagliardo, D. Dalli, W.-H. Huang, and P. Cosentino, Proof of Concept for Using Unmanned Aerial Vehicles for High Mast Pole and Bridge Inspections (Tech. Rep. BDV28-TWO-977-02). Tallahassee, FL: Florida Dept. Transp., Res. Center, 2015, http://dx.doi.org/10.13140/RG.2.1.4567.2166. http://dx.doi.org/10.13140/RG.2.1.4567.2... ], [¹¹11 J. Seo, L. Duque, and J. Wacker, “Glued-laminated timber arch bridge inspection using UAV,” in Comput. Civ. Eng. 2019: Smart Cities, Sustainability, and Resilience, Reston, 2019, pp. 336–342.], [⁴³43 J. Wells and B. Lovelace, Improving the Quality of Bridge Inspections Using Unmanned Aircraft Systems (UAS) (Tech. Rep. MN/RC 2018-26). St. Paul, MN: Minnesota Dept. Transp., 2018. [Online]. Available: https://trid.trb.org/view/1539868 https://trid.trb.org/view/1539868... ], [⁴⁵45 M. N. Gillins, “Unmanned aircraft systems for bridge inspection: testing and developing end-to-end operational workflow,” M.S. thesis, Oregon State Univ., Corvallis, OR, USA, 2016. [Online]. Available: http://hdl.handle.net/1957/60001 http://hdl.handle.net/1957/60001... ], [⁵⁴54 S. Dorafshan, R. J. Thomas, C. Coopmans, and M. Maguire, "A practitioner’s guide to small unmanned aerial systems for bridge inspection," Infrastruct., vol. 4, no. 4, pp. 72, 2019, http://dx.doi.org/10.3390/infrastructures4040072. http://dx.doi.org/10.3390/infrastructure... ], [⁵⁸58 M. N. Gillins, D. T. Gillins, and C. Parrish, “Cost-effective bridge safety inspections using unmanned aircraft systems (UAS),” in Geotech. Struct. Eng. Congr., Phoenix, AZ, USA, 2016, pp. 1931–1940.], [⁶²62 J. Seo, L. Duque, and J. Wacker, "Drone-enabled bridge inspection methodology and application," Autom. Construct., vol. 94, pp. 112–126, 2018, http://dx.doi.org/10.1016/j.autcon.2018.06.006. http://dx.doi.org/10.1016/j.autcon.2018.... ]–[⁶⁵65 J. Seo, L. Duque, and J. P. Wacker, "Field application of UAS-based bridge inspection," Transp. Res. Rec., vol. 2672, no. 12, pp. 72–81, 2018, http://dx.doi.org/10.1177/0361198118780825. http://dx.doi.org/10.1177/03611981187808... ], [⁶⁷67 L. Duque, “UAV-based bridge inspection and computational simulations,” M.S. thesis, Dept. Civ. Environ. Eng., South Dakota State Univ., Brookings, SD, USA, 2017.], [⁸⁰80 J. Seo, E. Jeong, and J. Wacker, “Visual bridge damage measurement using drone-captured image quality optimization,” in Eur. Workshop Struct. Health Monit., P. Rizzo and A. Milazzo, Eds., Cham: Springer International Publishing, 2021, pp. 503–513, http://dx.doi.org/10.1007/978-3-030-64908-1_47. http://dx.doi.org/10.1007/978-3-030-6490... ], [⁸¹81 E. Jeong, J. Seo, and J. Wacker, “Grayscale drone inspection image enhancement framework for advanced bridge defect measurement,” Transp. Res. Rec., vol. 2675, no. 8, pp. 603–612, 2021.], [¹¹⁷117 C.-H. Yang, M.-C. Wen, Y.-C. Chen, and S.-C. Kang, “An optimized unmanned aerial system for bridge inspection,” in 2015 Proc. 32nd Int. Symp. Automat. Robot. Constr. (ISARC), Oulu, Finland, 2015, pp. 1–6.], [¹³⁰130 M. Yamada, M. Nakao, Y. Hada, and N. Sawasaki, “Development and field test of novel two-wheeled UAV for bridge inspections,” in 2017 Int. Conf. Unmanned Aircr. Syst. (ICUAS), Miami, FL, USA, 2017, pp. 1014–1021.], [¹³⁷137 P. J. Sarkis and J. M. Sarkis, “Uso de drone em inspeção e definição de recuperação em OAEs,” in 9th Cong. Bras. Pontes e Estrut. (CBPE), Rio de Janeiro, Brazil, 2016, pp. 1–10.]	[⁴⁶46 S. Dorafshan, R. J. Thomas, C. Coopmans, and M. Maguire, “Deep learning neural networks for sUAS-assisted structural inspections: feasibility and application,” in 2018 Int. Conf. Unmanned Aircr. Syst. (ICUAS), Dallas, TX, USA, 2018, pp. 874–882.]–[⁴⁸48 S. Dorafshan, M. Maguire, N. V. Hoffer, and C. Coopmans, Fatigue Crack Detection Using Unmanned Aerial Systems in Under-Bridge Inspection (Tech. Rep. FHWA-ITD-17-256). Boise, Idaho: Idaho Transp. Dept., 2017.], [⁵⁰50 B. J. Perry, “Streamlined bridge inspection framework utilizing unmanned aerial vehicles (UAVs),” M.S. thesis, Dept. Civ. Environ. Eng., Colorado State Univ., Fort Collins, CO, USA, 2019. [Online]. Available: https://hdl.handle.net/10217/197417 https://hdl.handle.net/10217/197417... ]–[⁵³53 Y. Xu and Y. Turkan, "BrIM and UAS for bridge inspections and management,” Eng. Constr. Archit. Manage., vol. 27, no. 3, pp. 785–807, 2020, http://dx.doi.org/10.1108/ECAM-12-2018-0556. http://dx.doi.org/10.1108/ECAM-12-2018-0... ], [⁵⁵55 B. J. Perry, Y. Guo, R. Atadero, and J. W. van de Lindt, "Unmanned aerial vehicle (UAV)-enabled bridge inspection framework,” in Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations, H. Yokota and D.M. Frangopol, Eds., London: CRC Press, 2021, pp. 158–165.], [⁵⁶56 B. J. Perry, Y. Guo, R. Atadero, and J. W. van de Lindt, "Tracking bridge condition over time using recurrent UAV-based inspection,” in Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations. CRC Press, 2021, pp. 286–291.], [⁶⁰60 J. J. Lin, A. Ibrahim, S. Sarwade, and M. Golparvar-Fard, "Bridge inspection with aerial robots: Automating the entire pipeline of visual data capture, 3D mapping, defect detection, analysis, and reporting," J. Comput. Civ. Eng., vol. 35, no. 2, pp. 04020064, 2021, http://dx.doi.org/10.1061/(ASCE)CP.1943-5487.0000954. http://dx.doi.org/10.1061/(ASCE)CP.1943-... ], [⁶¹61 W. Wu, M. A. Qurishee, J. Owino, I. Fomunung, M. Onyango, and B. Atolagbe, “Coupling deep learning and UAV for infrastructure condition assessment automation,” in 2018 IEEE Int. Smart Cities Conf. (ISC2), Kansas City, MO, USA, 2018, pp. 1–7.], [⁶⁸68 A. Humpe, "Bridge inspection with an off-the-shelf 360º camera drone," Drones, vol. 4, no. 4, pp. 67, 2020, http://dx.doi.org/10.3390/drones4040067. http://dx.doi.org/10.3390/drones4040067... ], [⁷¹71 I.-H. Kim, H. Jeon, S.-C. Baek, W.-H. Hong, and H.-J. Jung, "Application of crack identification techniques for an aging concrete bridge inspection using an unmanned aerial vehicle," Sens., vol. 18, no. 6, pp. 1881, 2018, http://dx.doi.org/10.3390/s18061881. http://dx.doi.org/10.3390/s18061881... ], [⁷⁴74 Y.-F. Liu, X. Nie, J.-S. Fan, and X.-G. Liu, "Image-based crack assessment of bridge piers using unmanned aerial vehicles and three-dimensional scene reconstruction," Comput. Aided Civ. Infrastruct. Eng., vol. 35, no. 5, pp. 511–529, 2020, http://dx.doi.org/10.1111/mice.12501. http://dx.doi.org/10.1111/mice.12501... ], [⁷⁶76 Y. Z. Ayele, M. Aliyari, D. Griffiths, and E. L. Droguett, "Automatic crack segmentation for UAV-assisted bridge inspection," Energies, vol. 13, no. 23, pp. 6250, 2020, http://dx.doi.org/10.3390/en13236250. http://dx.doi.org/10.3390/en13236250... ], [⁷⁸78 Z. Yu, Y. Shen, and C. Shen, "A real-time detection approach for bridge cracks based on YOLOv4-FPM," Autom. Construct., vol. 122, pp. 103514, 2021, http://dx.doi.org/10.1016/j.autcon.2020.103514. http://dx.doi.org/10.1016/j.autcon.2020.... ], [⁹⁰90 G. Morgenthal et al., "Framework for automated UAS-based structural condition assessment of bridges," Autom. Construct., vol. 97, pp. 77–95, 2019, http://dx.doi.org/10.1016/j.autcon.2018.10.006. http://dx.doi.org/10.1016/j.autcon.2018.... ], [⁹⁶96 A. Ellenberg, A. Kontsos, F. Moon, and I. Bartoli, "Bridge related damage quantification using unmanned aerial vehicle imagery," Struct. Contr. Health Monit., vol. 23, no. 9, pp. 1168–1179, 2016, http://dx.doi.org/10.1002/stc.1831. http://dx.doi.org/10.1002/stc.1831... ], [¹⁰⁴104 Q. Zhu, T. H. Dinh, M. D. Phung, and Q. P. Ha, “Hierarchical convolutional neural network with feature preservation and autotuned thresholding for crack detection,” IEEE Access, vol. 9, pp. 60201–60214, 2021.]–[¹¹²112 H.-F. Wang, L. Zhai, H. Huang, L.-M. Guan, K.-N. Mu, and G. Wang, "Measurement for cracks at the bottom of bridges based on tethered creeping unmanned aerial vehicle," Autom. Construct., vol. 119, pp. 103330, 2020, http://dx.doi.org/10.1016/j.autcon.2020.103330. http://dx.doi.org/10.1016/j.autcon.2020.... ], [¹¹⁵115 S. Yoon, G.-H. Gwon, J.-H. Lee, and H.-J. Jung, "Three-dimensional image coordinate-based missing region of interest area detection and damage localization for bridge visual inspection using unmanned aerial vehicles," Struct. Health Monit., vol. 20, no. 4, pp. 1462–1475, 2021, http://dx.doi.org/10.1177/1475921720918675. http://dx.doi.org/10.1177/14759217209186... ], [¹¹⁶116 B. Lei, Y. Ren, N. Wang, L. Huo, and G. Song, "Design of a new low-cost unmanned aerial vehicle and vision-based concrete crack inspection method," Struct. Health Monit., vol. 19, no. 6, pp. 1871–1883, 2020, http://dx.doi.org/10.1177/1475921719898862. http://dx.doi.org/10.1177/14759217198988... ], [¹³¹131 X. Peng, X. Zhong, C. Zhao, A. Chen, and T. Zhang, "A UAV-based machine vision method for bridge crack recognition and width quantification through hybrid feature learning," Constr. Build. Mater., vol. 299, pp. 123896, 2021, http://dx.doi.org/10.1016/j.conbuildmat.2021.123896. http://dx.doi.org/10.1016/j.conbuildmat.... ]–[¹³⁶136 J.-H. Lee, S.-S. Yoon, I.-H. Kim, and H.-J. Jung, “Study on image quality assessment and processing, damage diagnosis of crack for bridge inspection based on unmanned aerial vehicle,” in ACEM18/Struct. 18, Incheon, Korea, 2018, pp. 1–6. [Online]. Available: http://hdl.handle.net/10203/247365 http://hdl.handle.net/10203/247365... ], [¹⁴³143 J.-W. Kim, S.-B. Kim, J.-C. Park, and J.-W. Nam, “Development of crack detection system with unmanned aerial vehicles and digital image processing,” in 2015 World Congr. Adv. Struct. Eng. Mechanics (ASEM), Incheon, Korea, 2015, pp. 1–11.], [¹⁵⁰150 J. Dang and P. Chun, "Mixed training of deep convolutional neural network for bridge deterioration detection with UAV and inspection report sourced images,” in Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations. CRC Press, 2021, pp. 308–312.]
	Spalling	[¹¹11 J. Seo, L. Duque, and J. Wacker, “Glued-laminated timber arch bridge inspection using UAV,” in Comput. Civ. Eng. 2019: Smart Cities, Sustainability, and Resilience, Reston, 2019, pp. 336–342.], [⁶²62 J. Seo, L. Duque, and J. Wacker, "Drone-enabled bridge inspection methodology and application," Autom. Construct., vol. 94, pp. 112–126, 2018, http://dx.doi.org/10.1016/j.autcon.2018.06.006. http://dx.doi.org/10.1016/j.autcon.2018.... ]–[⁶⁵65 J. Seo, L. Duque, and J. P. Wacker, "Field application of UAS-based bridge inspection," Transp. Res. Rec., vol. 2672, no. 12, pp. 72–81, 2018, http://dx.doi.org/10.1177/0361198118780825. http://dx.doi.org/10.1177/03611981187808... ], [⁶⁷67 L. Duque, “UAV-based bridge inspection and computational simulations,” M.S. thesis, Dept. Civ. Environ. Eng., South Dakota State Univ., Brookings, SD, USA, 2017.], [⁸¹81 E. Jeong, J. Seo, and J. Wacker, “Grayscale drone inspection image enhancement framework for advanced bridge defect measurement,” Transp. Res. Rec., vol. 2675, no. 8, pp. 603–612, 2021.]	[⁶⁰60 J. J. Lin, A. Ibrahim, S. Sarwade, and M. Golparvar-Fard, "Bridge inspection with aerial robots: Automating the entire pipeline of visual data capture, 3D mapping, defect detection, analysis, and reporting," J. Comput. Civ. Eng., vol. 35, no. 2, pp. 04020064, 2021, http://dx.doi.org/10.1061/(ASCE)CP.1943-5487.0000954. http://dx.doi.org/10.1061/(ASCE)CP.1943-... ], [⁶¹61 W. Wu, M. A. Qurishee, J. Owino, I. Fomunung, M. Onyango, and B. Atolagbe, “Coupling deep learning and UAV for infrastructure condition assessment automation,” in 2018 IEEE Int. Smart Cities Conf. (ISC2), Kansas City, MO, USA, 2018, pp. 1–7.], [¹⁰⁵105 L. Yang, B. Li, W. Li, H. Brand, B. Jiang, and J. Xiao, "Concrete defects inspection and 3D mapping using CityFlyer quadrotor robot,” IEEE/CAA J," Autom. Sin., vol. 7, no. 4, pp. 991–1002, 2020, http://dx.doi.org/10.1109/JAS.2020.1003234. http://dx.doi.org/10.1109/JAS.2020.10032... ], [¹⁰⁶106 L. Yang, B. Li, W. Li, Z. Liu, G. Yang, and J. Xiao, “A robotic system towards concrete structure spalling and crack database,” in 2017 IEEE Int. Conf. Robot. Biomimetics (ROBIO), Macau, China, 2017, pp. 1276–1281.], [¹⁰⁸108 X. Peng, X. Zhong, C. Zhao, Y. F. Chen, and T. Zhang, "The feasibility assessment study of bridge crack width recognition in images based on special inspection UAV," Adv. Civ. Eng., vol. 2020, pp. 1–17, 2020., http://dx.doi.org/10.1155/2020/8811649. http://dx.doi.org/10.1155/2020/8811649... ], [¹¹⁵115 S. Yoon, G.-H. Gwon, J.-H. Lee, and H.-J. Jung, "Three-dimensional image coordinate-based missing region of interest area detection and damage localization for bridge visual inspection using unmanned aerial vehicles," Struct. Health Monit., vol. 20, no. 4, pp. 1462–1475, 2021, http://dx.doi.org/10.1177/1475921720918675. http://dx.doi.org/10.1177/14759217209186... ], [¹³²132 H.-J. Jung, J.-H. Lee, S. Yoon, and I.-H. Kim, "Bridge inspection and condition assessment using unmanned aerial vehicles (UAVs): major challenges and solutions from a practical perspective," Smart Struct. Syst., vol. 24, no. 5, pp. 669–681, 2019, http://dx.doi.org/10.12989/sss.2019.24.5.669. http://dx.doi.org/10.12989/sss.2019.24.5... ], [¹³⁴134 Z. Ma, E. Zhao, G. Granello, and G. Loporcaro, "Drone aided machine-learning tool for post-earthquake bridge damage reconnaissance,” in 17th World Conf. Earthq. Eng. (WCEE), Sendai, Japan, 2020, pp. 1–12. [Online]. Available: https://hdl.handle.net/10092/101461 https://hdl.handle.net/10092/101461... ]
	Efflorescence	[¹⁰10 L. D. Otero, N. Gagliardo, D. Dalli, W.-H. Huang, and P. Cosentino, Proof of Concept for Using Unmanned Aerial Vehicles for High Mast Pole and Bridge Inspections (Tech. Rep. BDV28-TWO-977-02). Tallahassee, FL: Florida Dept. Transp., Res. Center, 2015, http://dx.doi.org/10.13140/RG.2.1.4567.2166. http://dx.doi.org/10.13140/RG.2.1.4567.2... ], [¹¹11 J. Seo, L. Duque, and J. Wacker, “Glued-laminated timber arch bridge inspection using UAV,” in Comput. Civ. Eng. 2019: Smart Cities, Sustainability, and Resilience, Reston, 2019, pp. 336–342.], [⁴³43 J. Wells and B. Lovelace, Improving the Quality of Bridge Inspections Using Unmanned Aircraft Systems (UAS) (Tech. Rep. MN/RC 2018-26). St. Paul, MN: Minnesota Dept. Transp., 2018. [Online]. Available: https://trid.trb.org/view/1539868 https://trid.trb.org/view/1539868... ], [⁴⁵45 M. N. Gillins, “Unmanned aircraft systems for bridge inspection: testing and developing end-to-end operational workflow,” M.S. thesis, Oregon State Univ., Corvallis, OR, USA, 2016. [Online]. Available: http://hdl.handle.net/1957/60001 http://hdl.handle.net/1957/60001... ], [⁵⁴54 S. Dorafshan, R. J. Thomas, C. Coopmans, and M. Maguire, "A practitioner’s guide to small unmanned aerial systems for bridge inspection," Infrastruct., vol. 4, no. 4, pp. 72, 2019, http://dx.doi.org/10.3390/infrastructures4040072. http://dx.doi.org/10.3390/infrastructure... ], [⁵⁸58 M. N. Gillins, D. T. Gillins, and C. Parrish, “Cost-effective bridge safety inspections using unmanned aircraft systems (UAS),” in Geotech. Struct. Eng. Congr., Phoenix, AZ, USA, 2016, pp. 1931–1940.], [⁶²62 J. Seo, L. Duque, and J. Wacker, "Drone-enabled bridge inspection methodology and application," Autom. Construct., vol. 94, pp. 112–126, 2018, http://dx.doi.org/10.1016/j.autcon.2018.06.006. http://dx.doi.org/10.1016/j.autcon.2018.... ]–[⁶⁵65 J. Seo, L. Duque, and J. P. Wacker, "Field application of UAS-based bridge inspection," Transp. Res. Rec., vol. 2672, no. 12, pp. 72–81, 2018, http://dx.doi.org/10.1177/0361198118780825. http://dx.doi.org/10.1177/03611981187808... ], [⁶⁷67 L. Duque, “UAV-based bridge inspection and computational simulations,” M.S. thesis, Dept. Civ. Environ. Eng., South Dakota State Univ., Brookings, SD, USA, 2017.], [⁸¹81 E. Jeong, J. Seo, and J. Wacker, “Grayscale drone inspection image enhancement framework for advanced bridge defect measurement,” Transp. Res. Rec., vol. 2675, no. 8, pp. 603–612, 2021.]	[⁶⁰60 J. J. Lin, A. Ibrahim, S. Sarwade, and M. Golparvar-Fard, "Bridge inspection with aerial robots: Automating the entire pipeline of visual data capture, 3D mapping, defect detection, analysis, and reporting," J. Comput. Civ. Eng., vol. 35, no. 2, pp. 04020064, 2021, http://dx.doi.org/10.1061/(ASCE)CP.1943-5487.0000954. http://dx.doi.org/10.1061/(ASCE)CP.1943-... ], [¹³²132 H.-J. Jung, J.-H. Lee, S. Yoon, and I.-H. Kim, "Bridge inspection and condition assessment using unmanned aerial vehicles (UAVs): major challenges and solutions from a practical perspective," Smart Struct. Syst., vol. 24, no. 5, pp. 669–681, 2019, http://dx.doi.org/10.12989/sss.2019.24.5.669. http://dx.doi.org/10.12989/sss.2019.24.5... ]
	Exposed rebar	[¹¹11 J. Seo, L. Duque, and J. Wacker, “Glued-laminated timber arch bridge inspection using UAV,” in Comput. Civ. Eng. 2019: Smart Cities, Sustainability, and Resilience, Reston, 2019, pp. 336–342.], [⁶²62 J. Seo, L. Duque, and J. Wacker, "Drone-enabled bridge inspection methodology and application," Autom. Construct., vol. 94, pp. 112–126, 2018, http://dx.doi.org/10.1016/j.autcon.2018.06.006. http://dx.doi.org/10.1016/j.autcon.2018.... ], [⁶⁴64 J. Seo, J. P. Wacker, and L. Duque, Evaluating the Use of Drones for Timber Bridge Inspection (Tech. Rep.). Madison, WI: US Dept. Agriculture, Forest Service, Forest Products Lab., 2018.], [⁶⁵65 J. Seo, L. Duque, and J. P. Wacker, "Field application of UAS-based bridge inspection," Transp. Res. Rec., vol. 2672, no. 12, pp. 72–81, 2018, http://dx.doi.org/10.1177/0361198118780825. http://dx.doi.org/10.1177/03611981187808... ], [⁶⁷67 L. Duque, “UAV-based bridge inspection and computational simulations,” M.S. thesis, Dept. Civ. Environ. Eng., South Dakota State Univ., Brookings, SD, USA, 2017.], [⁷⁰70 E. Ciampa, L. D. Vito, and M. R. Pecce, "Practical issues on the use of drones for construction inspections," J. Phys. Conf. Ser., vol. 1249, no. 1, pp. 012016, May 2019, http://dx.doi.org/10.1088/1742-6596/1249/1/012016. http://dx.doi.org/10.1088/1742-6596/1249... ]	[⁶⁰60 J. J. Lin, A. Ibrahim, S. Sarwade, and M. Golparvar-Fard, "Bridge inspection with aerial robots: Automating the entire pipeline of visual data capture, 3D mapping, defect detection, analysis, and reporting," J. Comput. Civ. Eng., vol. 35, no. 2, pp. 04020064, 2021, http://dx.doi.org/10.1061/(ASCE)CP.1943-5487.0000954. http://dx.doi.org/10.1061/(ASCE)CP.1943-... ], [¹⁰⁸108 X. Peng, X. Zhong, C. Zhao, Y. F. Chen, and T. Zhang, "The feasibility assessment study of bridge crack width recognition in images based on special inspection UAV," Adv. Civ. Eng., vol. 2020, pp. 1–17, 2020., http://dx.doi.org/10.1155/2020/8811649. http://dx.doi.org/10.1155/2020/8811649... ], [¹¹⁵115 S. Yoon, G.-H. Gwon, J.-H. Lee, and H.-J. Jung, "Three-dimensional image coordinate-based missing region of interest area detection and damage localization for bridge visual inspection using unmanned aerial vehicles," Struct. Health Monit., vol. 20, no. 4, pp. 1462–1475, 2021, http://dx.doi.org/10.1177/1475921720918675. http://dx.doi.org/10.1177/14759217209186... ], [¹⁵⁰150 J. Dang and P. Chun, "Mixed training of deep convolutional neural network for bridge deterioration detection with UAV and inspection report sourced images,” in Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations. CRC Press, 2021, pp. 308–312.]
	Generic/others	[¹⁰10 L. D. Otero, N. Gagliardo, D. Dalli, W.-H. Huang, and P. Cosentino, Proof of Concept for Using Unmanned Aerial Vehicles for High Mast Pole and Bridge Inspections (Tech. Rep. BDV28-TWO-977-02). Tallahassee, FL: Florida Dept. Transp., Res. Center, 2015, http://dx.doi.org/10.13140/RG.2.1.4567.2166. http://dx.doi.org/10.13140/RG.2.1.4567.2... ], [¹¹11 J. Seo, L. Duque, and J. Wacker, “Glued-laminated timber arch bridge inspection using UAV,” in Comput. Civ. Eng. 2019: Smart Cities, Sustainability, and Resilience, Reston, 2019, pp. 336–342.], [⁵⁴54 S. Dorafshan, R. J. Thomas, C. Coopmans, and M. Maguire, "A practitioner’s guide to small unmanned aerial systems for bridge inspection," Infrastruct., vol. 4, no. 4, pp. 72, 2019, http://dx.doi.org/10.3390/infrastructures4040072. http://dx.doi.org/10.3390/infrastructure... ], [⁶²62 J. Seo, L. Duque, and J. Wacker, "Drone-enabled bridge inspection methodology and application," Autom. Construct., vol. 94, pp. 112–126, 2018, http://dx.doi.org/10.1016/j.autcon.2018.06.006. http://dx.doi.org/10.1016/j.autcon.2018.... ]–[⁶⁵65 J. Seo, L. Duque, and J. P. Wacker, "Field application of UAS-based bridge inspection," Transp. Res. Rec., vol. 2672, no. 12, pp. 72–81, 2018, http://dx.doi.org/10.1177/0361198118780825. http://dx.doi.org/10.1177/03611981187808... ], [⁶⁷67 L. Duque, “UAV-based bridge inspection and computational simulations,” M.S. thesis, Dept. Civ. Environ. Eng., South Dakota State Univ., Brookings, SD, USA, 2017.], [⁸⁰80 J. Seo, E. Jeong, and J. Wacker, “Visual bridge damage measurement using drone-captured image quality optimization,” in Eur. Workshop Struct. Health Monit., P. Rizzo and A. Milazzo, Eds., Cham: Springer International Publishing, 2021, pp. 503–513, http://dx.doi.org/10.1007/978-3-030-64908-1_47. http://dx.doi.org/10.1007/978-3-030-6490... ], [⁸¹81 E. Jeong, J. Seo, and J. Wacker, “Grayscale drone inspection image enhancement framework for advanced bridge defect measurement,” Transp. Res. Rec., vol. 2675, no. 8, pp. 603–612, 2021.]	[⁶⁰60 J. J. Lin, A. Ibrahim, S. Sarwade, and M. Golparvar-Fard, "Bridge inspection with aerial robots: Automating the entire pipeline of visual data capture, 3D mapping, defect detection, analysis, and reporting," J. Comput. Civ. Eng., vol. 35, no. 2, pp. 04020064, 2021, http://dx.doi.org/10.1061/(ASCE)CP.1943-5487.0000954. http://dx.doi.org/10.1061/(ASCE)CP.1943-... ], [¹⁰⁸108 X. Peng, X. Zhong, C. Zhao, Y. F. Chen, and T. Zhang, "The feasibility assessment study of bridge crack width recognition in images based on special inspection UAV," Adv. Civ. Eng., vol. 2020, pp. 1–17, 2020., http://dx.doi.org/10.1155/2020/8811649. http://dx.doi.org/10.1155/2020/8811649... ], [¹³²132 H.-J. Jung, J.-H. Lee, S. Yoon, and I.-H. Kim, "Bridge inspection and condition assessment using unmanned aerial vehicles (UAVs): major challenges and solutions from a practical perspective," Smart Struct. Syst., vol. 24, no. 5, pp. 669–681, 2019, http://dx.doi.org/10.12989/sss.2019.24.5.669. http://dx.doi.org/10.12989/sss.2019.24.5... ], [¹⁵⁰150 J. Dang and P. Chun, "Mixed training of deep convolutional neural network for bridge deterioration detection with UAV and inspection report sourced images,” in Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations. CRC Press, 2021, pp. 308–312.]
Steel	Corrosion	[¹⁰10 L. D. Otero, N. Gagliardo, D. Dalli, W.-H. Huang, and P. Cosentino, Proof of Concept for Using Unmanned Aerial Vehicles for High Mast Pole and Bridge Inspections (Tech. Rep. BDV28-TWO-977-02). Tallahassee, FL: Florida Dept. Transp., Res. Center, 2015, http://dx.doi.org/10.13140/RG.2.1.4567.2166. http://dx.doi.org/10.13140/RG.2.1.4567.2... ], [¹¹11 J. Seo, L. Duque, and J. Wacker, “Glued-laminated timber arch bridge inspection using UAV,” in Comput. Civ. Eng. 2019: Smart Cities, Sustainability, and Resilience, Reston, 2019, pp. 336–342.], [⁴⁵45 M. N. Gillins, “Unmanned aircraft systems for bridge inspection: testing and developing end-to-end operational workflow,” M.S. thesis, Oregon State Univ., Corvallis, OR, USA, 2016. [Online]. Available: http://hdl.handle.net/1957/60001 http://hdl.handle.net/1957/60001... ], [⁵⁴54 S. Dorafshan, R. J. Thomas, C. Coopmans, and M. Maguire, "A practitioner’s guide to small unmanned aerial systems for bridge inspection," Infrastruct., vol. 4, no. 4, pp. 72, 2019, http://dx.doi.org/10.3390/infrastructures4040072. http://dx.doi.org/10.3390/infrastructure... ], [⁶²62 J. Seo, L. Duque, and J. Wacker, "Drone-enabled bridge inspection methodology and application," Autom. Construct., vol. 94, pp. 112–126, 2018, http://dx.doi.org/10.1016/j.autcon.2018.06.006. http://dx.doi.org/10.1016/j.autcon.2018.... ]–[⁶⁵65 J. Seo, L. Duque, and J. P. Wacker, "Field application of UAS-based bridge inspection," Transp. Res. Rec., vol. 2672, no. 12, pp. 72–81, 2018, http://dx.doi.org/10.1177/0361198118780825. http://dx.doi.org/10.1177/03611981187808... ], [⁶⁷67 L. Duque, “UAV-based bridge inspection and computational simulations,” M.S. thesis, Dept. Civ. Environ. Eng., South Dakota State Univ., Brookings, SD, USA, 2017.], [⁷⁰70 E. Ciampa, L. D. Vito, and M. R. Pecce, "Practical issues on the use of drones for construction inspections," J. Phys. Conf. Ser., vol. 1249, no. 1, pp. 012016, May 2019, http://dx.doi.org/10.1088/1742-6596/1249/1/012016. http://dx.doi.org/10.1088/1742-6596/1249... ], [¹⁰¹101 P. Darby, W. Hollerman, and J. Miller, "Exploring the potential utility of unmanned aerial vehicles for practical bridge inspection in Louisiana," MATEC Web Conf., vol. 271, pp. 01001, 2019, http://dx.doi.org/10.1051/matecconf/201927101001. http://dx.doi.org/10.1051/matecconf/2019... ], [¹⁰²102 P. Darby and V. Gopu, Bridge Inspecting with Unmanned Aerial Vehicles R&D (Tech. Rep. 17STLSU11). Baton Rouge, LA: Transp. Consortium of South-Central States (Tran-SET), 2018.]	[⁹⁶96 A. Ellenberg, A. Kontsos, F. Moon, and I. Bartoli, "Bridge related damage quantification using unmanned aerial vehicle imagery," Struct. Contr. Health Monit., vol. 23, no. 9, pp. 1168–1179, 2016, http://dx.doi.org/10.1002/stc.1831. http://dx.doi.org/10.1002/stc.1831... ], [¹⁰⁰100 R. Ali, D. Kang, G. Suh, and Y.-J. Cha, "Real-time multiple damage mapping using autonomous UAV and deep faster region-based neural networks for GPS-denied structures," Autom. Construct., vol. 130, pp. 103831, 2021, http://dx.doi.org/10.1016/j.autcon.2021.103831. http://dx.doi.org/10.1016/j.autcon.2021.... ], [¹⁰⁹109 P. Chun et al., "Utilization of unmanned aerial vehicle, artificial intelligence, and remote measurement technology for bridge inspections," J. Robot. Mechatron., vol. 32, no. 6, pp. 1244–1258, 2020, http://dx.doi.org/10.20965/jrm.2020.p1244. http://dx.doi.org/10.20965/jrm.2020.p124... ], [¹³⁸138 T. Harweg, A. Peters, D. Bachmann, and F. Weichert, "CNN-based deep architecture for health monitoring of civil and industrial structures using UAVs," Proc., vol. 42, no. 1, pp. 69, 2020, http://dx.doi.org/10.3390/ecsa-6-06640. http://dx.doi.org/10.3390/ecsa-6-06640... ], [¹⁴⁷147 A. Marchewka, P. Ziółkowski, and V. Aguilar-Vidal, "Framework for structural health monitoring of steel bridges by computer vision," Sens., vol. 20, no. 3, pp. 700, 2020, http://dx.doi.org/10.3390/s20030700. http://dx.doi.org/10.3390/s20030700... ], [¹⁵⁰150 J. Dang and P. Chun, "Mixed training of deep convolutional neural network for bridge deterioration detection with UAV and inspection report sourced images,” in Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations. CRC Press, 2021, pp. 308–312.], [¹⁵¹151 J. Shi, J. Dang, and R. Zuo, "Bridge damage cropping-and-stitching segmentation using fully convolutional network based on images from UAVs,” in Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations. CRC Press, 2021, pp. 264–270.]
	Crack	[⁴⁹49 S. Dorafshan, R. J. Thomas, and M. Maguire, "Fatigue crack detection using unmanned aerial systems in fracture critical inspection of steel bridges," J. Bridge Eng., vol. 23, no. 10, pp. 04018078, 2018, http://dx.doi.org/10.1061/(ASCE)BE.1943-5592.0001291. http://dx.doi.org/10.1061/(ASCE)BE.1943-... ], [⁵⁴54 S. Dorafshan, R. J. Thomas, C. Coopmans, and M. Maguire, "A practitioner’s guide to small unmanned aerial systems for bridge inspection," Infrastruct., vol. 4, no. 4, pp. 72, 2019, http://dx.doi.org/10.3390/infrastructures4040072. http://dx.doi.org/10.3390/infrastructure... ]	[⁴⁸48 S. Dorafshan, M. Maguire, N. V. Hoffer, and C. Coopmans, Fatigue Crack Detection Using Unmanned Aerial Systems in Under-Bridge Inspection (Tech. Rep. FHWA-ITD-17-256). Boise, Idaho: Idaho Transp. Dept., 2017.], [¹⁰⁰100 R. Ali, D. Kang, G. Suh, and Y.-J. Cha, "Real-time multiple damage mapping using autonomous UAV and deep faster region-based neural networks for GPS-denied structures," Autom. Construct., vol. 130, pp. 103831, 2021, http://dx.doi.org/10.1016/j.autcon.2021.103831. http://dx.doi.org/10.1016/j.autcon.2021.... ], [¹³⁸138 T. Harweg, A. Peters, D. Bachmann, and F. Weichert, "CNN-based deep architecture for health monitoring of civil and industrial structures using UAVs," Proc., vol. 42, no. 1, pp. 69, 2020, http://dx.doi.org/10.3390/ecsa-6-06640. http://dx.doi.org/10.3390/ecsa-6-06640... ]
	Paint failure	[⁴⁵45 M. N. Gillins, “Unmanned aircraft systems for bridge inspection: testing and developing end-to-end operational workflow,” M.S. thesis, Oregon State Univ., Corvallis, OR, USA, 2016. [Online]. Available: http://hdl.handle.net/1957/60001 http://hdl.handle.net/1957/60001... ], [⁸⁰80 J. Seo, E. Jeong, and J. Wacker, “Visual bridge damage measurement using drone-captured image quality optimization,” in Eur. Workshop Struct. Health Monit., P. Rizzo and A. Milazzo, Eds., Cham: Springer International Publishing, 2021, pp. 503–513, http://dx.doi.org/10.1007/978-3-030-64908-1_47. http://dx.doi.org/10.1007/978-3-030-6490... ], [¹⁴⁹149 J. Dang et al., "Arial patrol for bridge routine and post-earthquake emergency inspection using small aerial photography UAV,” in Proc. 5th Int. Symp. Adv. Civ. Environ. Eng. Practises Sustain. Develop. (ACEPS), Galle, Sri Lanka, 2017, pp. 260–267.]	[¹³⁸138 T. Harweg, A. Peters, D. Bachmann, and F. Weichert, "CNN-based deep architecture for health monitoring of civil and industrial structures using UAVs," Proc., vol. 42, no. 1, pp. 69, 2020, http://dx.doi.org/10.3390/ecsa-6-06640. http://dx.doi.org/10.3390/ecsa-6-06640... ]

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[1] Corresponding author: Fabio Yukio Toriumi. E-mail: fabioyt@usp.br