Lightweight detection model for coal gangue identification based on improved YOLOv5s

Focusing on the issues of complex models, high computational cost, and low identification speed of existing coal gangue image identification object detection algorithms, an optimized YOLOv5s lightweight detection model for coal gangue is proposed. Using ShuffleNetV2 as the backbone network, a convolution pooling module is used at the input end instead of the original convolution module. Combining the re-parameterization idea of RepVGG and introducing depthwise separable convolution, a neck feature fusion network is constructed. And using the WIoU function as the loss function. The experimental findings indicate that the improved model maintains the same accuracy, the number of parameters is only 5.1% of the original, the computational effort is reduced to 6.3 % of the original, and the identification speed is improved by 30.9% on GPU and 4 times on CPU. This method significantly reduces model complexity and improves detection speed while maintaining detection accuracy.