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Automatic Crack Detection and Classification Method for Subway Tunnel Safety Monitoring

School of Electronic and Information Engineering, Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing 100044, China
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Sensors 2014, 14(10), 19307-19328; https://doi.org/10.3390/s141019307
Received: 13 June 2014 / Revised: 20 August 2014 / Accepted: 3 October 2014 / Published: 16 October 2014
(This article belongs to the Section Physical Sensors)
Cracks are an important indicator reflecting the safety status of infrastructures. This paper presents an automatic crack detection and classification methodology for subway tunnel safety monitoring. With the application of high-speed complementary metal-oxide-semiconductor (CMOS) industrial cameras, the tunnel surface can be captured and stored in digital images. In a next step, the local dark regions with potential crack defects are segmented from the original gray-scale images by utilizing morphological image processing techniques and thresholding operations. In the feature extraction process, we present a distance histogram based shape descriptor that effectively describes the spatial shape difference between cracks and other irrelevant objects. Along with other features, the classification results successfully remove over 90% misidentified objects. Also, compared with the original gray-scale images, over 90% of the crack length is preserved in the last output binary images. The proposed approach was tested on the safety monitoring for Beijing Subway Line 1. The experimental results revealed the rules of parameter settings and also proved that the proposed approach is effective and efficient for automatic crack detection and classification. View Full-Text
Keywords: crack detection; crack classification; subway tunnel; line scan cameras crack detection; crack classification; subway tunnel; line scan cameras
MDPI and ACS Style

Zhang, W.; Zhang, Z.; Qi, D.; Liu, Y. Automatic Crack Detection and Classification Method for Subway Tunnel Safety Monitoring. Sensors 2014, 14, 19307-19328.

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