ABSTRACT Coal gangue sorting is a critical process in the clean production of coal. To address the issues of high computational complexity and deployment challenges in coal gangue object detection tasks, this paper proposes the SML-Neck structure, which incorporates the KernelWarehouse, LSK module, and MPDIoU to enhance YOLOv8s, resulting in the lightweight SKL-YOLOv8s model. Experiments were conducted using a self-constructed dataset comprising 14,790 images of coal gangue. The experimental results demonstrate that the proposed method surpasses YOLOv5s, YOLOv5×, YOLOv7, YOLOv7×, SSD, Faster-RCNN, RT-DETR-l, RT-DETR-x, and YOLOv8s in both accuracy and speed. Compared to the original model, it achieves a 60.9% reduction in computational cost, a 49.5% improvement in inference speed, and a decrease in information transmission loss from 2.6 to 0.6. The GradGAM visualization algorithm reveals that the improved model concentrates its attention more effectively on the entire target, enhancing the network’s ability to distinguish between objects and background. This method contributes to advancing clean coal development and provides a significant reference for coal gangue sorting based on object detection.
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