Abstract:

The four common pests in the citrus planting process were analyzed, and the detection problems of the pests with different sizes and low contrast features were improved on the basis of the Faster R-CNN model. Aiming at the problem that the down-sampling of the pooling layer leads to a decrease in the resolution of the detected image, which in turn leads to the loss of feature information in the image, the hole convolution method is used to capture more deep-level features in the image and increase the receptive field. Combined with the feature pyramid network FPN to fuse the features of different scales in the data, enhance the robustness of the features, and solve the problem that the original RPN network only uses a single final output layer for detection, and the detection accuracy is low. Compared with the original Faster R-CNN model, YOLOv4 and other classic target detection models, the mAP of the improved scheme is 91.72%, and the detection accuracy is improved. The experimental results prove that the proposed improvement scheme can meet the demand of identifying citrus pests in natural environments.

Key words: dilated convolution, FPN, Faster R-CNN, citrus pest, object detection