| Citation: |
LI Tianwen, LI Gongquan, LI Juntao. Integrated and improved YOLOv8 and triangulated network method for extracting indicators in open-pit mine mining areas[J]. Journal of Mine Automation，2025，51（4）：19-27. DOI: 10.13272/j.issn.1671-251x.2024110088
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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%.
| [1] |
张兆长,李岩,南贵军. 河北省非金属露天矿山水平分层式开采探索与实践[J]. 中国矿业,2024,33(6):203-209. DOI: 10.12075/j.issn.1004-4051.20240222
ZHANG Zhaochang,LI Yan,NAN Guijun. Exploration and practice of horizontal slicing mining in non-metal open-pit mines in Hebei Province[J]. China Mining Magazine,2024,33(6):203-209. DOI: 10.12075/j.issn.1004-4051.20240222
|
| [2] |
TOMBE R,VIRIRI S. Remote sensing image scene classification:advances and open challenges[J]. Geomatics,2023,3(1):137-155. DOI: 10.3390/geomatics3010007
|
| [3] |
JI Haowei,LUO Xianqi. Implementation of ensemble deep learning coupled with remote sensing for the quantitative analysis of changes in arable land use in a mining area[J]. Journal of the Indian Society of Remote Sensing,2021,49(11):2875-2890. DOI: 10.1007/s12524-021-01430-6
|
| [4] |
XIANG Jie,CHEN Jianping,SOFIA G,et al. Open-pit mine geomorphic changes analysis using multi-temporal UAV survey[J]. Environmental Earth Sciences,2018,77(6). DOI: 10.1007/s12665-018-7383-9.
|
| [5] |
LIN C H,WANG Tingyou. A novel convolutional neural network architecture of multispectral remote sensing images for automatic material classification[J]. Signal Processing:Image Communication,2021,97. DOI: 10.1016/j.image.2021.116329.
|
| [6] |
ZHANG Wei,TANG Ping,ZHAO Lijun. Remote sensing image scene classification using CNN-CapsNet[J]. Remote Sensing,2019,11(5). DOI: 10.3390/rs11050494.
|
| [7] |
LIU Yanfei,ZHONG Yanfei,FEI Feng,et al. Scene semantic classification based on random-scale stretched convolutional neural network for high-spatial resolution remote sensing imagery[C]. IEEE International Geoscience and Remote Sensing Symposium,Beijing,2016:763-766.
|
| [8] |
BALANIUK R,ISUPOVA O,REECE S. Mining and tailings dam detection in satellite imagery using deep learning[J]. Sensors,2020,20(23). DOI:10.3390/ s20236936.
|
| [9] |
XIE Hongbin,PAN Yongzhuo,LUAN Jinhua,et al. Semantic segmentation of open pit mining area based on remote sensing shallow features and deep learning[C]. Big Data Analytics for Cyber-Physical System in Smart City,Shanghai,2020:52-59.
|
| [10] |
CHEN Tao,ZHENG Xiaoxiong,NIU Ruiqing,et al. Open-pit mine area mapping with Gaofen-2 satellite images using U-net+[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2022,15:3589-3599. DOI: 10.1109/JSTARS.2022.3171290
|
| [11] |
XIE Hongbin,PAN Yongzhuo,LUAN Jinhua,et al. Open-pit mining area segmentation of remote sensing images based on DUSegNet[J]. Journal of the Indian Society of Remote Sensing,2021,49(6):1257-1270. DOI: 10.1007/s12524-021-01312-x
|
| [12] |
LIU Yong,LI Cheng,HUANG Jiade,et al. MineSDS:a unified framework for small object detection and drivable area segmentation for open-pit mining scenario[J]. Sensors,2023,23(13). DOI: 10.3390/s23135977.
|
| [13] |
MENG Xiaoliang,ZHANG Ding,DONG Sijun,et al. Open-pit granite mining area extraction using UAV aerial images and the novel GIPNet[J]. Remote Sensing,2024,16(5). DOI: 10.3390/rs16050789.
|
| [14] |
郭栋梁,张延军. 基于轻量化PAM−M−YOLO 模型的煤矸石图像检测[J]. 矿业研究与开发,2024,44(5):220-227.
GUO Dongliang,ZHANG Yanjun. Coal gangue image detection based on light-weighted PAM-M-YOLO model[J]. Mining Research and Development,2024,44(5):220-227.
|
| [15] |
阮顺领,鄢盛钰,顾清华,等. 基于多特征融合的露天矿区道路负障碍检测[J]. 煤炭学报,2024,49(5):2561-2572.
RUAN Shunling,YAN Shengyu,GU Qinghua,et al. Negative obstacle detection on open pit roads based on multi-feature fusion[J]. Journal of China Coal Society,2024,49(5):2561-2572.
|
| [16] |
LIU Yichao,SHAO Zongru,HOFFMANN N. Global attention mechanism:retain information to enhance channel-spatial interactions[EB/OL]. [2024-10-15]. https://arxiv.org/abs/2112.05561v1.
|
| [17] |
HAO Wangli,REN Chao,HAN Meng,et al. Cattle body detection based on YOLOv5-EMA for precision livestock farming[J]. Animals,2023,13(22). DOI: 10.3390/ani13223535.
|
| [18] |
REN Zili,WANG Liguan,HE Zhengxiang. Open-pit mining area extraction from high-resolution remote sensing images based on EMANet and FC-CRF[J]. Remote Sensing,2023,15(15). DOI: 10.3390/rs15153829.
|
| [19] |
ZHANG Hao,ZHANG Shuaijie. Focaler-IoU:more focused intersection over union loss[EB/OL]. [2024-10-15]. https://arxiv.org/abs/2401.10525v1.
|
| [20] |
胡佳乐,周敏,申飞. 面向无人机小目标的RTDETR改进检测算法[J]. 计算机工程与应用,2024,60(20):198-206. DOI: 10.3778/j.issn.1002-8331.2404-0114
HU Jiale,ZHOU Min,SHEN Fei. Improved detection algorithm of RTDETR for UAV small target[J]. Computer Engineering and Applications,2024,60(20):198-206. DOI: 10.3778/j.issn.1002-8331.2404-0114
|
| [21] |
OUYANG Daliang,HE Su,ZHANG Guozhong,et al. Efficient multi-scale attention module with cross-spatial learning[C].The 48th IEEE International Conference on Acoustics,Speech and Signal Processing,Rhodes,2023:1-5.
|
| [22] |
易磊,黄哲玮,易雅雯. 改进YOLOv8的输电线路异物检测方法[J]. 电子测量技术,2024,47(15):125-134.
YI Lei,HUANG Zhewei,YI Yawen. Improved YOLOv8 foreign object detection method for transmission lines[J]. Electronic Measurement Technology,2024,47(15):125-134.
|
| [23] |
李德永,王国法,郭永存,等. 基于CFS−YOLO算法的复杂工况环境下煤矸图像识别方法[J]. 煤炭科学技术,2024,52(6):226-237. DOI: 10.12438/cst.2023-1967
LI Deyong,WANG Guofa,GUO Yongcun,et al. Image recognition method of coal gangue in complex working conditions based on CES-YOLO algorithm[J]. Coal Science and Technology,2024,52(6):226-237. DOI: 10.12438/cst.2023-1967
|
| [24] |
谭笑,朱博,王晋扬,等. 一种面向离散高程点的高效TIN生长改进算法[J]. 海军工程大学学报,2023,35(2):25-30. DOI: 10.7495/j.issn.1009-3486.2023.02.004
TAN Xiao,ZHU Bo,WANG Jinyang,et al. An efficient improved TIN growth algorithm for discrete elevation points[J]. Journal of Naval University of Engineering,2023,35(2):25-30. DOI: 10.7495/j.issn.1009-3486.2023.02.004
|
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