基于样本自动标注的隧道裂缝病害智能识别

王耀东; 朱力强; 余祖俊; 史红梅; 折昌美

doi:10.3969/j.issn.0258-2724.20210092

基于样本自动标注的隧道裂缝病害智能识别

doi: 10.3969/j.issn.0258-2724.20210092

王耀东^{1, 2,},
朱力强^{1, 2},
余祖俊^{1, 2},
史红梅^{1, 2},
折昌美³

1.
北京交通大学智慧高铁系统前沿科学中心，北京 100044
2.
北京交通大学载运工具先进制造与测控技术教育部重点实验室，北京 100044
3.
中国科学院微电子研究所，北京 100029

基金项目: 中央高校基本科研业务费（M21JB00010）；科技部国家重点研发计划（2016YFB1200402）

详细信息

作者简介:
王耀东（1982—），男，副教授，研究方向为轨道交通智能检测，E-mail：ydwang@bjtu.edu.cn

中图分类号: TP394.1；TH691.9
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- 文章访问数: 402
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- PDF下载量: 102
- 被引次数: 0
出版历程
- 收稿日期: 2021-02-08
- 修回日期: 2021-06-21
- 网络出版日期: 2023-05-30
- 刊出日期: 2021-07-06

Intelligent Tunnel Crack Recognition Based on Automatic Sample Labeling

WANG Yaodong^{1, 2
,},
ZHU Liqiang^{1, 2},
YU Zujun^{1, 2},
SHI Hongmei^{1, 2},
SHE Changmei³

1.
Frontiers Science Center for Smart High-Speed Railway System, Beijing Jiaotong University, Beijing 100044, China
2.
Ministry of Education, Key Laboratory of Vehicle Advanced Manufacturing, Measuring and Control Technology, Beijing Jiaotong University, Beijing 100044, China
3.
Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China

摘要

摘要:
隧道表面裂缝的检测已经成为地铁运营人员的重要巡检任务之一. 为实现隧道裂缝病害的自动监测，提出一种结合病害特征提取和深度学习的隧道裂缝样本自动标注与识别算法；针对隧道裂缝形态特征建立裂缝图像的特征样本库，改进了AlexNet深度卷积网络结构；设计研制了轨道移动式隧道图像采集系统以及巡检车，采集并构建了包含4500张裂缝图像样本和1500张测试图像的数据集，用以验证算法的可行性和有效性. 研究结果表明：采集的图像清晰度符合要求，所设计算法可完成裂缝目标自动标注；裂缝图像测试集的识别率达到97.8%，证明了算法研究和采集系统的有效性.
- 图像采集 /
- 图像处理 /
- 裂缝病害 /
- 深度学习 /
- 样本标注
Abstract:
Detecting tunnel surface cracks has been one of the important tasks for subway operators. To achieve the automatic detection of tunnel cracks, this paper proposed an automatic labeling and recognition algorithm for tunnel crack samples, which combined crack feature extraction with deep learning. The paper also established an image feature library of crack samples based on the feature of tunnel cracks and improved the structure of the deep convolution network, namely AlexNet. In addition, the paper designed a track-sliding tunnel image acquisition system and inspection vehicle and then established a dataset consisting of 4 500 crack image samples and 1 500 test images, so as to verify the feasibility and effectiveness of the algorithm. The result shows that the clarity of the collected images meets the requirements, and the designed algorithm can complete the automatic labeling of cracks. The recognition rate of the crack image dataset is 97.8%, which can verify the effectiveness of the algorithm and the acquisition system.
- image acquisition /
- image processing /
- cracks /
- deep learning /
- sample labeling

HTML全文

图 1 隧道裂缝图像采集与识别算法

Figure 1. Tunnel crack image acquisition and recognition algorithm

下载: 全尺寸图片幻灯片

图 2 外接矩形与比例扩充矩形原理图

Figure 2. External rectangle and proportional expanded rectangle

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图 3 改进的AlexNet深度卷积网络结构

Figure 3. Improved AlexNet deep convolution network

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图 4 隧道图像采集系统原理

Figure 4. Diagram of tunnel image acquisition system

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图 5 轨道移动式多目相机图像采集装置

Figure 5. Image acquisition device of track-sliding multi-view camera

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图 6 图像处理结果

Figure 6. Results of image processing

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图 7 隧道裂缝的二值图像样本数据

Figure 7. Binary image sample data of tunnel cracks

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图 8 提取的连通区域图像样本数据

Figure 8. Extracted connected region image sample data

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图 9 裂缝区域比例扩充图像样本数据

Figure 9. Sample data of crack region scale expansion images

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图 10 隧道图像裂缝病害测试集识别结果

Figure 10. Recognition results of tunnel image crack test set

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表 1 采集装置参数信息表

Table 1. Parameters of acquisition device

名称	参数
相机自身旋转角度/（º）	0～360
相机可平移距离/cm	0～15
光源可平移距离/cm	0～15
光源自身旋转角度（º）	0～360
采集装置平移距离/cm	±30
采集装置上下调节距离/cm	80～120

下载: 导出CSV

表 2 裂缝图像样本数据分类识别准确率

Table 2. Recognition accuracy of crack image sample data %

样本	本算法训练准确率	本算法测试准确率	SVM 训练准确率	SVM 测试准确率
原始图像样本	88.0	89.1	75.8	70.3
二值图像样本	80.7	80.4	80.6	74.5
裂缝外接矩形样本	95.4	94.0	88.5	84.9
裂缝区域扩充样本	98.6	97.8	89.0	87.8

下载: 导出CSV

参考文献(12)

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基于样本自动标注的隧道裂缝病害智能识别

doi: 10.3969/j.issn.0258-2724.20210092

作者简介: 王耀东（1982—），男，副教授，研究方向为轨道交通智能检测，E-mail：ydwang@bjtu.edu.cn

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Intelligent Tunnel Crack Recognition Based on Automatic Sample Labeling

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出版历程

目录

作者简介:
王耀东（1982—），男，副教授，研究方向为轨道交通智能检测，E-mail：ydwang@bjtu.edu.cn