Lightweight coal and gangue detection algorithm based on improved Yolov7-tiny

This paper proposes a lightweight coal and gangue detection algorithm based on Yolov7-tiny for the current target detection algorithms, which have problems such as large model computation, low detection accuracy and low real-time detection performance. Firstly, we make cuts for the ShuffleNetV2 network and replace the Stage1 and Stage2 layers with CONV convolutional blocks to construct a new backbone network that improves the speed of feature extraction. Secondly, the neck of the original model is redesigned by fusing the original two paths of up-sampling and down-sampling into one path, introducing the CBAM attention mechanism to further improve the information extraction, fusion and interaction capabilities of the model, and the detection heads are reduced from three to two to improve the real-time inference capability of the model. Finally, SIoU is used to replace the loss function of the original network to improve the convergence ability and robustness of the model. The experimental results show that the accuracy and FPS of the lightweight model proposed in this paper reach 0.981 and 208, respectively. The improved model has higher real-time and accuracy, and the reduction of computation provides the possibility of deploying it on the edge devices.