Boosted Mask R-CNN algorithm for accurately detecting strawberry plant canopies in the fields from low-altitude drone images

Abstract

The research constructs a novel structure by integrating two parts of online object detection pipelines into the current state-of-the-art Mask R-CNN algorithm to improve the detection performance. The DJI Mavic Air drone is used to collect low-altitude sensing images of strawberry plant canopies. The data augmentation method is employed to feed more instances into the original image dataset to boost the generalization and robustness of the detection model in the training procedure. A ResNet50 backbone combined with a feature pyramid network is presented to extract the features of strawberry plant canopies. The online hard example mining algorithm is introduced to mine hard samples to learn rich features and update model weights. Soft non-maximum suppression based on recursive application on the remaining detection boxes within the predefined overlap threshold is proposed to improve the performance of identifying the large complex overlapping area of strawberry plant canopies. The qualitative results demonstrate that the improved detection model had an AP50 of 96.9 and an AR of 78.5 on the test set, which are approximately 30% higher than the original values.

Keywords:
strawberry plant canopy; feature pyramid network; online hard examples mining; soft non-maximum suppression