Tunnel Crack Detection Method and Crack Image Processing Algorithm Based on Improved Retinex and Deep Learning

Tunnel cracks are the main factors that cause damage and collapse of tunnel structures. How to detect tunnel cracks efficiently and avoid safety accidents caused by tunnel cracks effectively is a research hotspot at present. In order to meet the need for efficient detection of tunnel cracks, the tun...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 23; no. 22; p. 9140
Main Authors: Wu, Jie, Zhang, Xiaoqian
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.11.2023
Subjects:
Accuracy
Algorithms
Cameras
Classification
crack segmentation
Cracks
deep learning
Efficiency
Image processing
Imaging systems
Lasers
Light
Methods
multi-scale Retinex decomposition
Neural networks
Photography
Semantics
tunnel cracks
Vision systems
Wavelet transforms
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:Tunnel cracks are the main factors that cause damage and collapse of tunnel structures. How to detect tunnel cracks efficiently and avoid safety accidents caused by tunnel cracks effectively is a research hotspot at present. In order to meet the need for efficient detection of tunnel cracks, the tunnel crack detection method based on improved Retinex and deep learning is proposed in this paper. The tunnel crack images collected by optical imaging equipment are used to improve the contrast information of tunnel crack images using the image enhancement algorithm, and this image enhancement algorithm has the function of multi-scale Retinex decomposition with improved central filtering. An improved VGG19 network model is constructed to achieve efficient segmentation of tunnel crack images through deep learning methods and then form the segmented binary image. The Zhang–Suen fast parallel-thinning method is used to obtain the skeleton map of the single-layer pixel, and the length and width information of the tunnel cracks are obtained. The feasibility and effectiveness of the proposed method are verified by experiments. Compared with other methods in the literature, the maximum deviation in the length of the tunnel crack is about 5 mm, and the maximum deviation in the width of the tunnel crack is about 0.8 mm. The experimental results show that the proposed method has a shorter detection time and higher detection accuracy. The research results of this paper can provide a strong basis for the health evaluation of tunnels.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s23229140