Crop Insect Pest Detection based on Dilated Multi-scale Attention U-Net

Abstract

Abstract Early detection and identification of insect pests is the premise and basis of scientific control and accurate utilization of Insect pesticides. Aiming at the problems of low detection accuracy and slow training speed of the existing crop Insect pest detection models, a dilated multi-scale attention U-Net (DMSAU-Net) model is constructed for crop Insect pest detection. In its encoder, dilated Inception is designed to replace the convolution layer in U-Net to extract the multi-scale features of insect pest images and improve the accuracy of the model. An attention module is added to its decoder to focus on the edge of the insect pest image and reduce the upsampling noise and accelerate model convergence. The results on the crop insect pest image dataset verify that the proposed method has high segmentation accuracy and good generalization ability, and can be applied to practical crop insect pest monitoring system.