一种基于分割掩码的隧道裂缝病害自动识别后处理算法

doi:10.11947/j.AGCS.2024.20240088

测绘学报 ›› 2024, Vol. 53 ›› Issue (9): 1715-1724.doi: 10.11947/j.AGCS.2024.20240088

• 精密工程测量 • 上一篇

一种基于分割掩码的隧道裂缝病害自动识别后处理算法

胡波¹^,²(), 陈翰新¹^,²(), 任松³, 屈英豪¹^,², 刘清屹¹^,², 涂歆玥³, 王大涛¹^,²

^1.重庆市测绘科学技术研究院，重庆　401120
^2.自然资源部智能城市时空信息与装备工程技术创新中心，重庆　401120
^3.重庆大学煤矿灾害动力学与控制国家重点实验室，重庆　400044

收稿日期:2024-03-05 发布日期:2024-10-16
通讯作者: 陈翰新 E-mail:hubo@cqkcy.com;chenhx@cqkcy.com
作者简介:胡波（1987—），男，博士，正高级工程师，研究方向为精密工程测量、智能测量装备、信息化。E-mail：hubo@cqkcy.com
基金资助:
重庆市自然科学基金面上项目(CSTB2022NSCQ-MSX1615);重庆市自然科学基金创新发展联合基金项目(CSTB2023NSCQ-LZX0122);重庆市规划和自然资源局科研项目(KJ-2021054)

A post-processing algorithm for automatic recognition of tunnel crack diseases based on segmentation masks

Bo HU¹^,²(), Hanxin CHEN¹^,²(), Song REN³, Yinghao QU¹^,², Qingyi LIU¹^,², Xinyue TU³, Datao WANG¹^,²

^1.Chongqing Institute of Surveying and Mapping Science and Technology, Chongqing 401120, China
^2.Smart City Spatial-Temporal Information and Equipment Engineering Technology Innovation Center of the Ministry of Natural Resources, Chongqing 401120, China
^3.State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing 400044, China

Received:2024-03-05 Published:2024-10-16
Contact: Hanxin CHEN E-mail:hubo@cqkcy.com;chenhx@cqkcy.com
About author:HU Bo (1987—), male, PhD, professorate senior engineer, majors in precision engineering surveying, intelligent measuring equipment and informatization. E-mail: hubo@cqkcy.com
Supported by:
Chongqing Natural Science Foundation General Project(CSTB2022NSCQ-MSX1615);Chongqing Natural Science Foundation Innovation and Development Joint Fund Project(CSTB2023NSCQ-LZX0122);Research Project of Chongqing Planning and Natural Resources Bureau(KJ-2021054)

摘要/Abstract

摘要：

随着交通运输网络的建设，建成的隧道数量及隧道运营年限日益增加，给隧道安全运营带来了很大的挑战。快速检测隧道衬砌裂缝病害并准确提取裂缝长宽特征，是实现隧道高效养护和安全运营的重要保障。本文提出了一种高效精准的隧道裂缝病害后处理算法，基于DeepLabV3+语义分割模型的预测分割掩码，以连通域判别细化算法与端点聚类实例区分算法处理掩码断裂情况，实现了隧道裂缝骨架精准提取和实例区分。以长度计算和灰度差异值宽度分类算法实现了裂缝长宽特征的计算，长宽计算准确率分别为92.2%与86.3%。

关键词: 裂缝病害, 语义分割, 智能计算, DeepLabV3+

Abstract:

With the construction of transportation networks, the number of completed tunnels and the increasing service life of tunnels have brought great challenges to the safe operation of tunnels. Rapid detection of tunnel lining cracks and accurate extraction of crack length and width characteristics is an important guarantee for achieving efficient maintenance and safe operation of tunnel. This article proposes an efficient and accurate post-processing algorithm for tunnel crack diseases, based on the prediction segmentation mask of DeepLabV3+ semantic segmentation model. The connected domain discrimination refinement algorithm and endpoint clustering instance differentiation algorithm are used to process the mask fracture situation, achieving accurate extraction of tunnel crack skeleton and instance differentiation. Finally, the length calculation and grayscale difference value width classification algorithm are used to calculate the crack length and width characteristics. The accuracy of length and width calculation is 92.2% and 86.3%, respectively.

Key words: crack disease, semantic segmentation, intelligent computing, DeepLabV3+

中图分类号:

P258

胡波, 陈翰新, 任松, 屈英豪, 刘清屹, 涂歆玥, 王大涛. 一种基于分割掩码的隧道裂缝病害自动识别后处理算法[J]. 测绘学报, 2024, 53(9): 1715-1724.

Bo HU, Hanxin CHEN, Song REN, Yinghao QU, Qingyi LIU, Xinyue TU, Datao WANG. A post-processing algorithm for automatic recognition of tunnel crack diseases based on segmentation masks[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(9): 1715-1724.

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输入尺寸	操作结构	扩展因子	膨胀率	输出矩阵深度
1024×1024×3	convd2d	—	—	32
512×512×32	inverted bottleneck	1	1	16
512×512×16	inverted bottleneck×2	6	1	24
256×256×24	inverted bottleneck×3	6	1	32
128×128×32	inverted bottleneck×4	6	2	64
128×128×64	inverted bottleneck×3	6	2	96
128×128×96	inverted bottleneck×3	6	4	160
128×128×160	inverted bottleneck	6	4	320

g₁取值	裂缝宽度类别
g₁<Gray1	0.2 mm≤W<0.6 mm
Gray1≤g₁≤Gray2	0.6 mm≤W≤1 mm
g₁>Gray2	W>1 mm

参数	取值
取值定位点数量N	10，裂缝骨架像素点数量小于等于200时 40，裂缝骨架像素点数量大于200时
单侧灰度取值点数量X	10.0
Gray1	11.7
Gray2	19.0

数据集	准确率	召回率	L_R/(%)	W_R/(%)	每张图处理时间/s
训练集	0.913	0.949	93.2	88.5	—
测试集	0.914	0.936	92.2	86.3	0.449

一种基于分割掩码的隧道裂缝病害自动识别后处理算法

A post-processing algorithm for automatic recognition of tunnel crack diseases based on segmentation masks

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