Identification Method of Crop Aphids Based on Bionic Attention

Abstract

The accurate identification of crop aphids is an important aspect of improving agricultural productivity. Aphids are characterised by small targets and a body colour similar to their surroundings. Even the most advanced detectors can experience problems such as low detection accuracy and a high number of missed detections. In this paper, a multi-stream target detection model is proposed for fast and accurate detection of crop aphids in complex backgrounds. First, inspired by the human visual system, we propose a bionic attention (BA) approach. Unlike previous strategies, we do not improve the model but input additional category labels as bionic information streams into the network at the network input stage to support mainstream recognition, which improves the detection effect. In addition, through web crawling and manual screening, we construct an aphid dataset containing 2059 images, named IP_Aphids, based on IP102. In this paper, we combine the proposed BA with a number of classical target detection models, including YOLOv5s, YOLOv7-tiny, YOLOv8n, SSD, and faster R-CNN, and we also compare the BA with the classical attention mechanisms CBAM and SE. This approach allows the effectiveness of the method to be evaluated from multiple perspectives. The results show that combining the bionic information flow of BA with the original mainstream information flow as inputs to the five target detection models improves the mAP by 2.2%, 7%, 2.7%, 1.78%, and 1.12%, respectively, compared to the models using only the original inputs. In addition, the mAP of the YOLOv8n_BA model is higher than that of the YOLOv8n, YOLOv8n_CBAM, and YOLOv8n_SE models by 4.6%, 3.3%, and 2.7%, respectively. This indicates that the BA proposed in this study is significantly better than the classical attention to improve crop aphid detection, which provides a reference for crop aphid-related research.