Abstract
Spodoptera frugiperda is a major transboundary migratory pest under global alert by the Food and Agriculture Organization (FAO) of the United Nations. The accurate identification and counting of trapped adults in the field are key technologies for achieving quantitative monitoring and precision pest control. However, precise recognition is challenged by issues such as scale loss and the presence of mixed insect species in trapping images. To address this, we constructed a field image dataset of trapped Spodoptera frugiperda adults and proposed an improved YOLOv5s-based detection method. The dataset was collected over a two-year sex pheromone monitoring campaign in eastern-central Yunnan, China, comprising 9550 labeled insects across six categories, and was split into training, validation, and test sets in an 8:1:1 ratio. In this study, YOLOv7, YOLOv8, Mask R-CNN, and DETR were selected as comparative baselines to evaluate the recognition of images containing Spodoptera frugiperda adults and other insect species. However, the complex backgrounds introduced by field trap photography adversely affected classification performance, resulting in a relatively modest average accuracy. Considering the additional requirement for model lightweighting, we further enhanced the YOLOv5s architecture by integrating Mosaic data augmentation and an adaptive anchor box strategy. Additionally, three attention mechanisms-SENet, CBAM, and Coordinate Attention (CA)-were embedded into the backbone to build a multidimensional attention comparison framework, demonstrating CBAM's superiority under complex backgrounds. Ultimately, the CBAM-YOLOv5 model achieved 97.8% mAP@0.5 for Spodoptera frugiperda identification, with recognition accuracy for other insect species no less than 72.4%. Based on the optimized model, we developed an intelligent recognition system capable of image acquisition, identification, and counting, offering a high-precision algorithmic solution for smart trapping devices.