工矿自动化2025，Vol.51Issue(4)：19-27,9.DOI:10.13272/j.issn.1671-251x.2024110088
整合改进YOLOv8与三角网的露天矿山采场指标提取方法
Integrated and improved YOLOv8 and triangulated network method for extracting indicators in open-pit mine mining areas
摘要
Abstract
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％.关键词
露天矿山/采场信息提取/改进YOLOv8/不规则三角网/空间注意力机制/目标识别/目标分割Key words
open-pit mine/mining field information extraction/improved YOLOv8/triangulated irregular network/spatial attention mechanism/object recognition/object segmentation分类
矿业与冶金引用本文复制引用
李天文,李功权,李俊涛..整合改进YOLOv8与三角网的露天矿山采场指标提取方法[J].工矿自动化,2025,51(4):19-27,9.基金项目
国家自然科学基金青年科学基金项目(41701537). （41701537）
