The Segmentation of Tunnel Faces in Underground Mines Based on the Optimized YOLOv5

Tunnel faces in underground mines, as the front line of mining, play an important role in both mine safety and mining intelligence. However, the engineering quality of tunnel faces is still evaluated based on visual observations by technicians, which cannot guarantee safety and real-time performance. Therefore, there is an urgent need for a more effective method to detect the quality of tunnel face engineering. In this study, a high-performance and accurate tunnel face segmentation model was developed by applying the YOLOv5-seg computer vision model to an underground mine. By optimizing a classic Chinese underground mine image dataset through Sobel preprocessing and improving the network structure of the YOLOv5-seg model using the SimAM module, good predictive performance was achieved for tunnel face segmentation, with values of 0.97, 0.89, 0.80, and 0.78, respectively, achieved for the pixel accuracy, Dice coefficient, mask intersection over union (IOU), and box IOU on the test set. And the performance of this model outperforms all YOLOv5 models and U-net in the same task of tunnel face segmentation. Model interpretation and visualization further demonstrated the positive effect of the SimAM module on the model, and, finally, the segmentation results were used to evaluate the tunnel face engineering. Overall, this study’s results provide a feasible, safe, and real-time method for accurately segmenting tunnel faces in underground mines and provide a reliable approach for data-driven applications of intelligent technology in mines in the future.