煤矿采空区地表裂缝双任务检测方法研究  

Dual-task model for ground crack detection in the goaf of coal mines

作　　者：陈锡明 姚鑫[1] 任开瑀 姚闯闯 周振凯 杨依林 CHEN Ximing;YAO Xin;REN Kaiyu;YAO Chuangchuang;ZHOU Zhenkai;YANG Yilin(Institute of Geomechanics,Chinese Academy of Geological Sciences,Beijing 100081,China;School of Geography and Information Engineering,China University of Geosciences,Wuhan 430078,China;School of Engineering and Technology,China University of Geosciences(Beijing),Beijing 100083,China)

机构地区：[1]中国地质科学院地质力学研究所,北京100081 [2]中国地质大学(武汉)地理与信息工程学院,武汉430078 [3]中国地质大学(北京)工程技术学院,北京100083

出　　处：《遥感学报》2024年第12期3271-3286,共16页NATIONAL REMOTE SENSING BULLETIN

基　　金：中国地质调查项目(编号:DD20230433);中国三峡公司项目(编号:YMJ(XLD)/(19)110)。

摘　　要：采空区地表裂缝识别对矿区的生产安全和生态环境治理具有重要意义。裂缝检测可以通过语义分割和边缘提取任务实现。已有的深度学习裂缝检测方法通常将其分开处理,无法为后续的几何参数计算和损害程度量化提供足够数据,并且没有考虑到两个任务之间的互补性。本文设计了一个双任务卷积神经网络Goaf-DTNet(Dual-Task Convolutional Neural Network for Goaf Crack Recognition)来同时获取裂缝面状分割和线性踪迹提取结果,通过建立两个任务之间的信息互补来提高模型的精度。在Goaf-DTNet的面状分割分支中,使用了多尺度特征融合提取鲁棒性特征。同时,在裂缝线性踪迹提取分支中,使用面状分割特征对线性踪迹特征提取过程进行引导,提高模型对裂缝的定位精度。为了验证方法的有效性,分别在自建的地表裂缝数据集和公共数据集上进行了实验,实验结果显示本文方法优于其他对比方法。消融实验结果也表明联合训练两个任务可以提升彼此的准确率。除此之外,在真实大场景图像上的实验结果表明本文方法在矿区地表裂缝监测中具有一定的实际应用潜力,可以为矿区监测提供有效数据。Automatic detection of ground cracks in the goaf of coal mines plays an important role in production security and ecological environment management.Given the complex background,variable geometry,and scale of cracks in coal mines,automatically detecting ground cracks in the goaf remains challenging.Crack extraction can be treated as a segmentation task from a global view or as a skeleton extraction task(boundary detection)from a local view.Many methods view crack extraction as a single task independently.However,these methods cannot produce enough data for the subsequent measurement and quantitative evaluation of the extent of the detriment of ground cracks.Moreover,they disregard the information interaction of different tasks,which could potentially improve accuracy and efficiency.To solve these problems,this study designed a dual-task Convolutional Neural Network(CNN)called dual-task CNN for goaf crack recognition(Goaf-DTNet)to automatically detect ground cracks by using unmanned aerial vehicle imagery with a high spatial resolution.In Goaf-DTNet,an atrous spatial pyramid pooling module is introduced to extract multiscale semantic information.In consideration of the characteristics of ground cracks in the goaf,a Multiscale Feature Fusion Module(MFFM)was designed for the crack segmentation branch to further integrate local and global contextual information.A Segmentation-Guided crack skeleton Feature extraction Module(SGFM)was used in the crack skeleton extraction branch to provide spatial information through the spatial attention mechanism.The proposed dual-task model can explicitly avoid parameter calculation in the shared layers,thus reducing the memory footprint and accelerating each task.Meanwhile,the complementary information from the communication between two tasks can improve the detection accuracy of each task.For the task of skeleton extraction,the F1-score and Intersection over Union(IoU)value are 0.71 and 0.55,respectively;the former is about 1%higher than the F1-score of the second best method,and th

关 键 词：遥感 煤矿 采空区 无人机 (UAV) 裂缝检测 卷积神经网络 (CNN) 多任务学习 深度学习 

分 类 号：P2[天文地球—测绘科学与技术]