Jujube defect recognition method based on boosted convolutional neural network

Abstract

In order to solve the problem of difficult and slow identification of jujube defects, a convolutional neural network model based on boosted EfficientNetv2 was proposed by taking the dry, cracked, broken and normal jujube in jujube as the research object. First, optimize the model structure, and set of the first Fused_MBConv in EfficientNetv2 3*3 convolution improved to parallel 1*1 convolution, 3*3 convolution and two serial 3*3 convolution. With reference to the CSPNet idea, one part of the convolved feature map in the MBConv module is directly spliced across channels, the other part is output through the dense block through the transition layer, and then spliced with the feature map in the first part; Then, the original Swish activation function is replaced by the optimal FReLU activation function; Finally, the Coordinate Attention module is introduced to embed the position information into the channel attention to optimize the model. The experimental results showed that the recognition rates of dry jujube, cracked jujube, broken jujube and normal jujube were 95.32%, 98.79%, 98.19% and 97.81% respectively, and the average recognition rate was 97.39%. Compared with other algorithms, the model has faster speed and higher recognition accuracy for defective jujube.

Keywords:
jujube; defect identification; convolutional neural network; activation function; attention mechanism