Automatic Crack Detection and Classification Method for Subway Tunnel Safety Monitoring
TLDR
This paper presents an automatic crack detection and classification methodology for subway tunnel safety monitoring and presents a distance histogram based shape descriptor that effectively describes the spatial shape difference between cracks and other irrelevant objects.Abstract:
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.read more
Citations
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Crack detection using image processing: A critical review and analysis
Arun Mohan,Sumathi Poobal +1 more
TL;DR: In this paper, a detailed survey is conducted to identify the research challenges and the achievements till in this field, and those research papers are reviewed based on the image processing techniques, objectives, accuracy level, error level, and the image data sets.
Journal ArticleDOI
Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring
TL;DR: An overview of recent advances in computer vision techniques as they apply to the problem of civil infrastructure condition assessment and some of the key challenges that persist toward the goal of automated vision-based civil infrastructure and monitoring are presented.
Journal ArticleDOI
DeepCrack: A deep hierarchical feature learning architecture for crack segmentation
TL;DR: A deep hierarchical convolutional neural network (CNN) is proposed, called as DeepCrack, to predict pixel-wise crack segmentation in an end-to-end method using both guided filtering and Conditional Random Fields methods to refine the final prediction results.
Journal ArticleDOI
Deep learning based image recognition for crack and leakage defects of metro shield tunnel
TL;DR: A novel image recognition algorithm for semantic segmentation of crack and leakage defects of metro shield tunnel using hierarchies of features extracted by fully convolutional network (FCN) can be employed to rapidly and accurately recognize defects for structure health monitoring and maintenance of metro Shield tunnels.
Journal ArticleDOI
A Fast Detection Method via Region‐Based Fully Convolutional Neural Networks for Shield Tunnel Lining Defects
Yadong Xue,Yicheng Li +1 more
TL;DR: A fully convolutional network (FCN) model for classification and detection of tunnel lining defects, inspired by the state‐of‐the‐art deep learning, is proposed and shown to be very fast and efficient.
References
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Morphological Image Analysis: Principles and Applications
TL;DR: This self-contained volume will be valuable to all engineers, scientists, and practitioners interested in the analysis and processing of digital images.
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Analysis of edge-detection techniques for crack identification in bridges
TL;DR: This paper provides a comparison of the effectiveness of four crack-detection techniques: fast Haar transform (FHT), fast Fourier transform, Sobel, and Canny and shows that the FHT was significantly more reliable than the other three edge-detector techniques in identifying cracks.