Maize pests identification based on improved YOLOv4-Tiny

Abstract

Agricultural pest identification occupies a key position in agricultural economy and development. The accurate identification of pests is the premise of agricultural pest control. In recent years, image processing technology and deep learning technology have rapidly developed. Some researches have been applied to the field of insect recognition. Thus, some insect recognition deep learning models with good recognition accuracy and speed have been established. However, there is still much room for improvement when they were applied to the insect monitoring system deployed in the field. Considering the target recognition accuracy and speed, this paper selects the target detection algorithm YOLOv4-Tiny as the base model for insect recognition. The major advances are the attention mechanism and Spatial Pyramidal Pooling (SPP) structure as shown in: applying Convolutional Block Attention Module (CBAM) reduce computation and number of parameters; adopting SPP structure multi-scale pooling of input feature layers which increases the perceptual field and improves the robustness of the model. The experimental results show that the improved YOLOv4-Tiny model can significantly enhance the insect recognition accuracy.