Research on Target Detection in Underground Coal Mines Based on Improved YOLOv5

In view of the underground coal mine environment, which uses mostly infrared cameras to sense the surrounding environment's temperature, the images formed have the problems of less texture information, more noise, and blurred images. The detection of Underground targets in coal mines using YOLOv5(Ucm-YOLOv5), a neural network for real-time detection of coal mines, is suggested in this document. This network is an improvement on YOLOv5. Firstly, PP-LCNet is used as the backbone network for enhancing the inference speed on the CPU side. Secondly, the Focus module is eliminated, and the shuffle_block module is used to replace the C3 module in YOLOv5, which reduces the computation while removing redundant operations. Finally, the Anchor is optimized while introducing H-swish as the activation function. The experimental results show that Ucm-YOLOv5 has 41% fewer model parameters and an 86% smaller model than YOLOv5. The algorithm has higher detection accuracy in underground coal mines, while the detection speed at the CPU side reaches the real-time detection standard, which meets the working requirements for target detection in underground coal mines.