YOLOv5s pruning method for edge computing of coal mine safety monitoring

At present, the combination of edge computing and machine vision has a good application prospect for coal mine safety monitoring. But the storage space and computing resources at the edge are limited, and high-precision complex visual models are difficult to deploy on it. In order to solve the above problems, a YOLOv5s pruning method based on indirect and direct evaluation space fusion (IDESF) is proposed for the edge end of coal mine safety monitoring, aiming to achieve lightweight YOLOv5s network. Firstly, a structural analysis is conducted on the convolutional layers of each module in the YOLOv5s network to determine the free pruning layer and conditional pruning layer. It lays the foundation for subsequent allocation of pruning rates and calculation of the number of pruning kernels. Secondly, the pruning rate is assigned to the prunable layers according to the convolutional kernel weight importance score based on the convolutional kernel weight magnitude and the relative computational complexity of the layers, which effectively reduces the computational complexity of the network after pruning. Thirdly, based on the direct importance evaluation criterion of convolutional kernels, the indirect output importance of convolutional layers is introduced into the direct importance space in the form of scaling factors. The position distribution of convolutional kernels is updated to construct a fused importance evaluation space that includes the output information and amplitude information of convolutional kernels. It thereby improves the comprehensiveness of convolutional kernel importance evaluation. Finally, drawing on the idea of topk voting, the process of median filtering for screening redundant convolution kernels is optimized. The method quantifies the degree of redundancy of a convolutional kernel in terms of the incidence of nodes in the adjacency matrix of a directed graph, which improves the interpretability and generality of the redundant convolutional kernel screening process. The experimental results show the following points. ① From the perspective of balancing model precision and lightweighting, YOLOV5s_IDESF with a pruning rate of 50% is the optimal lightweight YOLOv5s. On the VOC dataset, YOLOv5s_IDESF mAP@.5 and mAP@0.5 is the highest, reaching 0.72 and 0.44 respectively. The parameter count is reduced to a minimum of 2.65×10⁶, the computational complexity is reduced to 1.16×10⁹, and the overall complexity is also reduced to the lowest. The image processing frame rate reaches 31.15 frames per second. ② On the coal mine dataset, YOLOv5s_IDESF mAP@.5 and mAP@0.5∶0.95 achieve the highest values of 0.94 and 0.52, respectively. The parameter count is reduced to a minimum of 3.12×10⁶, the computational complexity is reduced to 1.24×10⁹, and the overall complexity is also minimized. The image processing frame rate reaches 31.55 frames per second.