DSW-YOLO-Based Green Pepper Detection Method Under Complex Environments

In this paper, a lightweight detection model DSW-YOLO based on improved YOLOv10n is proposed. After comparing mainstream lightweight models (YOLOv5n, YOLOv6n, YOLOv8n, YOLOv9t and YOLOv10n), YOLOv10n with the best performance was selected as the baseline. The DWRR block was then designed and integrated with the C2f module to form C2f-DWRR, replacing the original C2f blocks in the backbone. Consequently, the model’s P, R, mAP50, and mAP50-95 increased by 2.3%, 2.1%, 1.8%, and 3.4%, respectively, while the parameter count dropped by 0.16 M and the model size was reduced by 0.25 MB. A SimAM parameter-free attention mechanism was added to the last layer of the backbone, boosting P, R, mAP50, and mAP50-95 to 90.6%, 84.0%, 91.8%, and 68.5%, and reducing average detection time to 1.1 ms. The CIOU function was replaced with WIOUv3 to accelerate convergence, decrease loss, and significantly enhance detection performance. Experimental results show that on a custom green pepper dataset, DSW-YOLO outperformed the baseline by achieving gains of 2.9%, 2.7%, 2.2%, and 3.4% in P, R, mAP50, and mAP50-95, reducing parameters by 1.6 M, cutting inference time by 0.7 ms, and shrinking the model size to 5.31 MB. DSW-YOLO efficiently and accurately detects green peppers in complex field conditions, significantly improving detection accuracy while remaining lightweight, and provides theoretical and technical support for designing and optimizing pepper-picking robot vision systems.