Lightweight and accurate aphid detection model based on an improved deep-learning network

Abstract

Rapid and accurate detection of bamboo aphids can help prevent large-scale aphid infestations from occurring, which is of great significance for increasing bamboo shoot production and economic benefits. Herein, a lightweight and accurate model, SCA-YOLOv5s, was established by integrating ShuffleNetv2 and Coordinate Attention with the YOLOv5s model to detect Takecallis taiwanus on the yellow sticky traps. Specifically, we first replaced the backbone network of YOLOv5s with ShuffleNetv2 to reduce the number of parameters and computational complexity of the model. Second, an anchor optimization method was proposed by combining linear scaling and k-means algorithm to generate appropriate anchor boxes for detecting small-sized alate aphids. Third, the coordinate attention mechanism was added to the neck network to improve the feature extraction ability. To verify the performance of the proposed SCA-YOLOv5s model, eight detection models were constructed with existing deep learning methods, including SSD300, YOLOv3, Faster R-CNN, YOLOv4, YOLOv4-Tiny, YOLOX-Tiny, YOLOv7-Tiny, and YOLOv5s. Results reveal that the SCA-YOLOv5s model achieved higher detection accuracy than the other eight models. Its mean average precision reached 92.2 %. The proposed model has a size of only 6.7 MB, its floating-point operations (FLOPs) is 7.4 × 10⁹, its inference time is 6.6 ms, and compared with YOLOv5s, it is 53.47 % smaller in model size, 55.15 % lower in FLOPs, and 0.8 ms faster in inference time. The results indicate that the proposed model can maintain high detection accuracy while minimizing computation and inference time, which is crucial for deployment in remote areas with low information technology. This study provides valuable technical support for the control of aphids in bamboo forests.