一种用于浅埋深煤层采动地裂缝的无人机影像实例分割方法

doi:10.13474/j.cnki.11-2246.2025.1112

测绘通报 ›› 2025, Vol. 0 ›› Issue (11): 78-83.doi: 10.13474/j.cnki.11-2246.2025.1112

一种用于浅埋深煤层采动地裂缝的无人机影像实例分割方法

孙彬¹, 张瑞玲¹, 任辰锋¹, 林云浩², 孙超², 刘一涵², 刘梦洁², 袁德宝², 许志华²

1. 国能亿利能源有限责任公司, 内蒙古鄂尔多斯 017000;
2. 中国矿业大学(北京)地球科学与测绘工程学院, 北京 100083

收稿日期:2025-04-11 发布日期:2025-12-04
通讯作者: 许志华。E-mail:z.xu@cumtb.edu.cn
作者简介:孙彬(1979—),男,工程师,主要从事煤矿地测防治水技术管理工作。E-mail:1059345@ceic.com
基金资助:
国家自然科学基金(52174160);河北省自然科学基金生态智慧矿山联合基金(E2020402086);中央高校基本科研业务费专项资金(2023ZKPYDC11)

An instance segmentation method for mining-induced ground fissures in shallow coal seams using UAV imagery

SUN Bin¹, ZHANG Ruiling¹, REN Chenfeng¹, LIN Yunhao², SUN Chao², LIU Yihan², LIU Mengjie², YUAN Debao², XU Zhihua²

1. Guoneng Yili Energy Co., Ltd., Ordos 017000, China;
2. School of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China

Received:2025-04-11 Published:2025-12-04

摘要/Abstract

摘要： 浅埋深煤层开采诱发严重的矿区地表裂缝,增加了采空区遗煤自燃和雨季煤层涌水等灾害风险。为此,本文以无人机遥感影像为基础,提出了基于改进YOLOv8的浅埋深煤层开采地表裂缝实例分割方法。首先,将YOLOv8模型主干模块替换为PVT(pramid vision transformer)网络结构,用于学习稠密的采动地裂缝多尺度特征,增强对采动裂缝几何形态的识别能力。然后,选取内蒙古自治区黄玉川煤矿为研究区,制作了一套采动地裂缝数据集HYCdata,用于改进YOLOv8的模型训练和地裂缝分割试验。结果表明,本文改进的YOLOv8模型对采动地裂缝实例分割的精度优于原YOLOv8模型,其mAP_0.5为74.3%,可为浅埋深煤层开采地表裂缝自动识别与分割提供技术支撑。

关键词: 无人机影像, 黄玉川煤矿, 采动地裂缝, 实例分割, YOLOv8模型

Abstract: Ground fissures induced by shallow coal mining significantly damage the ecological environment of mining areas.Timely detection and landfill treatment can prevent secondary hazards such as spontaneous combustion of residual coal and water inrush during rainy seasons.This paper proposes an improved YOLOv8 model for instance segmentation of ground fissures from UAV imagery.Firstly,the backbone of YOLOv8 is replaced with a pyramid vision transformer (PVT) to enhance multi-scale and high-resolution feature learning for dense fissures,improving geometric recognition capabilities.Then,UAV images from the Huangyuchuan Coal Mine in Inner Mongolia were processed to create the HYCdata dataset for model training.Experiments demonstrate that the modified YOLOv8 outperforms the original model,achieving a mAP_0.5 of 74.3%,providing an effective solution for automatic segmentation of widespread mining-induced fissures.

Key words: UAV imagery, Huang Yuchuan mines, mining-induced ground fissures, instance segmentation, YOLOv8 network

中图分类号:

P237

孙彬, 张瑞玲, 任辰锋, 林云浩, 孙超, 刘一涵, 刘梦洁, 袁德宝, 许志华. 一种用于浅埋深煤层采动地裂缝的无人机影像实例分割方法[J]. 测绘通报, 2025, 0(11): 78-83.

SUN Bin, ZHANG Ruiling, REN Chenfeng, LIN Yunhao, SUN Chao, LIU Yihan, LIU Mengjie, YUAN Debao, XU Zhihua. An instance segmentation method for mining-induced ground fissures in shallow coal seams using UAV imagery[J]. Bulletin of Surveying and Mapping, 2025, 0(11): 78-83.

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一种用于浅埋深煤层采动地裂缝的无人机影像实例分割方法

An instance segmentation method for mining-induced ground fissures in shallow coal seams using UAV imagery

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