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Automatic Crack Detection and Classification Method for Subway Tunnel Safety Monitoring

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... Full description

Journal Title: Sensors 0, 2014, Vol.14(10), pp.19307-19328
Main Author: Zhang, Wenyu
Other Authors: Zhang, Zhenjiang , Qi, Dapeng , Liu, Yun
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: E-ISSN: 1424-8220 ; DOI: 10.3390/s141019307
Link: http://search.proquest.com/docview/1651394199/?pq-origsite=primo
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recordid: proquest1651394199
title: Automatic Crack Detection and Classification Method for Subway Tunnel Safety Monitoring
format: Article
creator:
  • Zhang, Wenyu
  • Zhang, Zhenjiang
  • Qi, Dapeng
  • Liu, Yun
subjects:
  • Subway Tunnels
  • Classification
  • Flaw Detection
  • Safety
  • Automation
  • Cracks
  • Images
  • Monitoring
  • Instruments and Measurements (So)
  • Design Principles (Mt)
  • Electric Components and Materials (Ea)
  • Crack Detection
  • Crack Classification
  • Subway Tunnel
  • Line Scan Cameras Crack Detection
  • Line Scan Cameras
ispartof: Sensors, 0, 2014, Vol.14(10), pp.19307-19328
description: 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.
language: eng
source:
identifier: E-ISSN: 1424-8220 ; DOI: 10.3390/s141019307
fulltext: fulltext
issn:
  • 14248220
  • 1424-8220
url: Link


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titleAutomatic Crack Detection and Classification Method for Subway Tunnel Safety Monitoring
creatorZhang, Wenyu ; Zhang, Zhenjiang ; Qi, Dapeng ; Liu, Yun
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identifierE-ISSN: 1424-8220 ; DOI: 10.3390/s141019307
subjectSubway Tunnels ; Classification ; Flaw Detection ; Safety ; Automation ; Cracks ; Images ; Monitoring ; Instruments and Measurements (So) ; Design Principles (Mt) ; Electric Components and Materials (Ea) ; Crack Detection ; Crack Classification ; Subway Tunnel ; Line Scan Cameras Crack Detection ; Line Scan Cameras
descriptionCracks 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.
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descriptionCracks 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.
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abstractCracks 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.
doi10.3390/s141019307
urlhttp://search.proquest.com/docview/1651394199/
date2014-01-01