Adaptive feature fusion pyramid network for multi-classes agricultural pest detection

Abstract

The accurate and robust crop pest detection system is an important step to enable the reliable forecasting of agricultural pest in the community of precision agriculture, attracting great attention in many countries. For achieving the automatic recognition and detection of agricultural pest, previous methods adopt image processing-based methods, leading to lower efficiency. Then, machine vision-based methods are introduced into crop pest detection by using hand-crafted feature descriptors, improving the detection precision and speed. However, the manual feature is powerless for precise recognition. Considering powerful ability of feature extraction of convolutional neural network(CNN), we have developed a CNN-based method for multi-classes pest detection under complex scenes. In this paper, an adaptive feature fusion is introduced into feature pyramid network for extracting richer pest features. Then, an adaptive augmentation module has been developed for reducing the information loss of the highest-level feature maps. Finally, a two-stage region-based convolutional neural network (R-CNN) was built for refining predicted bounding boxes, which can obtain the categories and locations of pests of each image. We have conducted large quantities of comparison experiments on AgriPest21 dataset. Our method could achieve an accuracy of 77.0%, which significantly outperforms other state-of-the-art methods, including SSD, RetinaNet, FPN, Dynamic R-CNN, and Cascade R-CNN.