基于 VMA-UNet 网络的露天矿多类型道路识别模型

金属矿山 ›› 2025, Vol. 55 ›› Issue (8): 137-149.

基于 VMA-UNet 网络的露天矿多类型道路识别模型

李若谦¹ 　梁　鹏^1,2 　刘　言¹ 　王羽冉¹ 　郑来耀¹ 　王金松¹ 　王聚贤¹

1. 华北理工大学矿业工程学院河北唐山 063210;2. 河北省矿山绿色智能开采技术创新中心河北唐山 063210

出版日期:2025-09-15 发布日期:2025-09-16
通讯作者: 梁　鹏(1987—),男,副教授,博士,硕士研究生导师。
作者简介:李若谦(2004—),男,本科生。
基金资助:
国家自然科学基金项目(编号:52474099);河北省创新能力提升计划项目(编号:23564201D);河北省自然科学基金项目(编号: D2024209014,E2024209024);国家级大学生创新创业训练计划项目(编号:202410081055)。

Multi-type Road Recognition Model of Open-pit Mine Based on VMA-UNet Network

LI Ruoqian ¹ 　 LIANG Peng ^1,2 　 LIU Yan ¹ 　 WANG Yuran ¹ 　 ZHENG Laiyao¹ 　 WANG Jinsong ¹ 　 WANG Juxian ¹

1. School of Mining Engineering,North China University of Science and Technology,Tangshan 063210,China; 2. Mine Green Intelligent Mining Technology Innovation Center of Hebei Province,Tangshan 063210,China

Online:2025-09-15 Published:2025-09-16

摘要/Abstract

摘要： 露天矿区道路的精确识别对于矿区数字化与无人智能化建设至关重要,但现有机器视觉方法难以精准识别阴影遮挡及碎石堆积道路。提出一种以 VGG16 替代原有 U-Net 骨干网络,结合偏振多尺度特征自注意力 (PMFS)和通道空间并行注意力(CSPA)机制,用于矿区无人机图像道路识别的 VMA-UNet 网络。使用无人机采集矿区道路影像,并将其划分为非结构化、阴影遮挡、碎石堆积道路 3 种类型,经 CLAHE 处理后用 Labelme 标注,制成多类型道路数据集并采用迁移学习预训练模型。对比试验表明,CLAHE 操作能够显著突出矿区不同类型道路所具有的特征,迁移学习使网络收敛速度更快,更好地适应不同场景下的矿区道路数据。 VMA-UNet 在各类矿区道路数据集中表现优异,在总数据集上的准确率、召回率和平均交并比分别达 94. 8%、91. 29%和 80. 58%,均优于 D-LinkNet、DeepLabV3+和 U-Net 网络模型,可精准识别阴影遮挡及碎石堆积道路的边缘,有效提升矿区多类型道路的识别精度。

关键词: 无人机　深度学习　露天矿多类型道路　迁移学习　道路识别　注意力机制

Abstract: The accurate identification of roads in open-pit mines is very important for the digitization and unmanned intelligent construction of mining areas. However,the existing machine vision methods are difficult to accurately identify shadow occlusion and gravel accumulation roads. A VMA-UNet network for road recognition of UAV images in mining areas is proposed,which replaces the original U-Net backbone network with VGG16,and combines polarization multi-scale feature self-attention (PMFS) and channel space parallel attention (CSPA) mechanisms. The UAV was used to collect the road images of the mining area,which were divided into three types:unstructured,shaded and gravel accumulation roads. After CLAHE processing,they were labeled with Labelme to make multi-type road data sets and transfer learning was used to pre-train the model. Comparative experiments show that the CLAHE operation can significantly highlight the characteristics of different types of roads in the mining area,and the transfer learning makes the network converge faster and better adapt to the mining area road data in different scenarios. VMA-UNet performs well in various mining area road datasets. The accuracy,recall and average intersection-union ratio on the total dataset are 94. 8%,91. 29% and 80. 58%,respectively. The indexes are better than D-LinkNet,DeepLabV3+ and U-Net network models,which can accurately identify the edge of shadow occlusion and gravel accumulation roads,and effectively improve the recognition accuracy of multi-type roads in mining areas.

Key words: UAV,deep learning,multi-type roads for open-pit mines,transfer learning,road recognition,attention mechanism

中图分类号:

TD854

李若谦　梁　鹏, 　刘　言　王羽冉　郑来耀　王金松　王聚贤. 基于 VMA-UNet 网络的露天矿多类型道路识别模型 [J]. 金属矿山, 2025, 55(8): 137-149.

LI Ruoqian 　 LIANG Peng , 　 LIU Yan 　 WANG Yuran 　 ZHENG Laiyao 　 WANG Jinsong 　 WANG Juxian . Multi-type Road Recognition Model of Open-pit Mine Based on VMA-UNet Network [J]. Metal Mine, 2025, 55(8): 137-149.

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