Double Self-Attention Based Fully Connected Feature Pyramid Network for Field Crop Pest Detection

Pest detection techniques are helpful in reducing the frequency and scale of pest outbreaks; however, their application in the actual agricultural production process is still challenging owing to the problems of inter-species similarity, multi-scale, and background complexity of pests. To address these problems, this study proposes an FD-YOLO pest target detection model. The FD-YOLO model uses a Fully Connected Feature Pyramid Network (FC-FPN) instead of a PANet in the neck, which can adaptively fuse multi-scale information so that the model can retain small-scale target features in the deep layer, enhance large-scale target features in the shallow layer, and enhance the multiplexing of effective features. A dual self-attention module (DSA) is then embedded in the C3 module of the neck, which captures the dependencies between the information in both spatial and channel dimensions, effectively enhancing global features. We selected 16 types of pests that widely damage field crops in the IP102 pest dataset, which were used as our dataset after data supplementation and enhancement. The experimental results showed that FD-YOLO’s mAP@0.5 improved by 6.8% compared to YOLOv5, reaching 82.6% and 19.1%–5% better than other state-of-the-art models. This method provides an effective new approach for detecting similar or multiscale pests in field crops.