Research on the Application of Convolutional Neural Network Based on YOLO Algorithm in Pest Small Target Detection

In agricultural science, preventing and controlling diseases and insect pests is a key research topic. Information technology has been applied to pest detection and identification. In crop pest research, neural networks can improve accuracy and efficiency by applying image recognition and classification technology. The small size of the pests in the disease pictures makes them easy to detect. The pest small target data set is constructed in this paper to train small target detection networks. A convolutional neural network based on the YOLO algorithm was proposed to detect small targets, such as pests, with few features. This paper also proposes a mosaic-based data enhancement method, label smoothing processing, and a backbone network improvement idea to improve the detection efficiency of the YOLOv5 model.