Integrated and improved YOLOv8 and triangulated network method for extracting indicators in open-pit mine mining areas

The research on remote sensing imagery of open-pit mines based on deep learning has provided a direction for the rapid identification and extraction of open-pit mining areas. However, its practical application in open-pit mining is still limited to the recognition stage, with issues such as inaccurate boundary extraction and unbalanced sample distribution during model training. To address these issues, an improved method for extracting mining field indicators by integrating YOLOv8 with a triangulated network was proposed. Based on YOLOv8, the following improvements were made to obtain Mine-YOLO: the addition of an Efficient Multi-Scale Attention (EMA) module to enhance the model's recognition and segmentation accuracy of mining field boundaries; the inclusion of a Global Attention Mechanism (GAM) module to retain open-pit mining field feature data at a global scale, improving target recognition accuracy; and the optimization of the Focaler-IoU loss function to enhance the model's ability to distinguish positive samples. By utilizing digital elevation model (DEM) data of the open-pit mine obtained by UAVs and combining it with the Mine-YOLO model for recognition and segmentation, DEM images of the mining area were obtained, and a triangulated irregular network was automatically generated. This enabled precise quantitative monitoring of the mining field's area, volume, and depth. Experimental results showed that the Mine-YOLO model achieved average accuracies of 0.942 for recognition and 0.865 for segmentation, demonstrating high recognition accuracy and good segmentation results. Practical application results showed that the mining field data extracted using the Mine-YOLO model were similar to traditional measurement values, with an average area error of 5.8%, an average volume error of 4.9%, and a minimum depth error of only 0.2%.