OS-YOLO: A robust object detection model for storage pest monitoring in American ginseng

Storage pest poses a serious threat to the quality and safety of Chinese medicinal materials such as American ginseng (Panax quinquefolium). However, intelligent detection of pests in ginseng storage remains challenging due to the lack of targeted datasets and models. Specifically, the detection task is hindered by the small size of pest targets, complex storage backgrounds, and poor robustness and generalization of existing models.This study propose OS-YOLO, an improved lightweight object detection model based on the YOLOv11n architecture, specifically optimized for pest monitoring in American ginseng storage. A specialized image dataset, Aginseng-Pest, was constructed, containing 10,207 annotated images across five key pest species under diverse lighting and background conditions. OS-YOLO integrates multiple advanced modules—including a Bidirectional Feature Pyramid Network (BiFPN), Omni-Dimensional Dynamic Convolution (ODConv), Tiny Object Detection Layer (TODL), DCO-CSPB, and DB-SEv2—for enhanced multi-scale feature fusion, tiny target recognition, and channel attention optimization. Experimental results show that OS-YOLO achieved a precision of 96.1%, recall of 91.6%, F1-score of 94.0%, mAP@0.5 of 96.2%, and mAP@0.5:0.95 of 76.3%, outperforming several state-of-the-art detection models.The proposed model significantly improves pest detection accuracy and robustness under complex storage scenarios, particularly for small and occluded insects. This study provides a key technical reference for intelligent pest prevention and control in the storage of Chinese medicinal materials.