Inspection equipment study for subway tunnel defects by grey-scale image processing

Huang, Hongwei; Sun, Yan; Xue, Yadong; Wang, Fei

doi:10.1016/j.aei.2017.03.003

Cited by 122 publications

(47 citation statements)

References 14 publications

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“…The technique can capably determine best or close to best solutions in excessive search spaces. The swarm molecule movement in the search space is demonstrated in the following equations: www.ijacsa.thesai.org From the equations (6) and 7, are the coefficients with the range of 2.0, are the independent random values developed in the limit between 0 and 1, is the velocity of i th particle. , represents the current position i and the optimal fitness value of the molecule at the present iteration, is the optimum global values in the swarm.…”

Section: Adaptive Thresholding With Psomentioning

confidence: 99%

“…The pixels of an image correspond one-to-one with points on the surface of object and be utilized for computing the defects positions. For inspecting the surface defects of tunnels [6] this method has been applied. The new-fangled unidentified defects require restructuring the automated existing algorithms [7] so as to distinguish and categorize new defects, causing extended development cycles, impediments and have need of human endeavor toward sustaining the design and improvement of the system constantly.…”

Section: Introductionmentioning

confidence: 99%

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A Modified Adaptive Thresholding Method using Cuckoo Search Algorithm for Detecting Surface Defects

Aslam¹,

Santhi²,

Ramasamy³

et al. 2019

IJACSA

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There are various mathematical optimization problems that can be effectively solved by meta-heuristic algorithms. The improvement of these algorithms is that they carry out iterative search processes which resourcefully act upon exploration and exploitation in spatial domain containing global and local optima. An innovative robust Cuckoo Optimization Algorithm (COA) with adaptive thresholding is proposed to solve the problem of detection and estimation of surface defects on metal coating surfaces. The proposed method is developed through implementing changes to COA and improved the performance. For improving capability of local search as well to keep the global search effect, the enhanced methods such as level set is associated with the proposed method. Also, the method adapts dynamic step size, adaptively changing with the search process for improving the rate of convergence and the ability of local search. The algorithm performance is scrutinized from the experimental analysis and results. Also, the segmentation effectiveness is further enhanced by adapting suitable methods for preprocessing and post processing. The comparison and analysis of the results accomplished with the proposed method and results of earlier methods shows superior performance of the proposed method.

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Section: Adaptive Thresholding With Psomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Modified Adaptive Thresholding Method using Cuckoo Search Algorithm for Detecting Surface Defects

Aslam¹,

Santhi²,

Ramasamy³

et al. 2019

IJACSA

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“…The tunnel lining images were obtained by a photographing vehicle equipped with line sensor cameras. It is a non-contact detection technology that can track the information of the tunnel surface in a very short time [26,27]. The process of extracting cracks was shown in Figure 9.…”

Section: Digital Inspection Testmentioning

confidence: 99%

“…Therefore, it is possible to describe the distribution of cracks and evaluate the states of tunnel lining by fractal theory. Moreover, the development of machine vision-based method makes it possible to collect the images of tunnel lining and to calculate the dimensions of the cracks efficiently [26,27].…”

Section: Introductionmentioning

confidence: 99%

A New Health Assessment Index of Tunnel Lining Based on the Digital Inspection of Surface Cracks

Wu¹,

Jiang²,

Wang³

et al. 2017

Preprint

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Abstract:The stability assessment of aged tunnel linings are mainly evaluated based on the visual inspection, and the Tunnel-lining Crack Index (TCI) is one of the most widely used tunnel lining health assessment indexes in Japan. However, the intersection and distribution of cracks, which could influence the stability of tunnel lining greatly, were not considered in TCI. A new method was proposed for the health assessment of tunnel lining, which evaluated the lining states according to the fractal dimension of cracks. Based on the machine vision-based method, the crack image could be extracted efficiently. The fractal dimension of lining cracks in one span could be obtained in a few minutes. A series of comparative tests and field tests were conducted to evaluate the validity of this new method. The comparative tests confirmed that fractal dimension was able to characterize the density, width, and distribution of cracks. The intersection of cracks, which would increase the risk of lining collapse, could also increase the fractal dimension. The fractal dimensions of tunnel lining cracks were obtained according to the digital inspection test of Hidake Tunnel in Japan for all the 65 spans. Moreover, the TCI was obtained through statistical methods. The correlation between fractal dimension and TCI of tunnel lining was studied. The significance of the new evaluation index is that it can identify the unusual spans of tunnel lining and provide a basis for further internal testing. As a complement to the conventional visual inspection method, the fractal dimension of the cracks is a promising health assessment index.

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“…An automatic crack detection and classification method for subway tunnel safety monitoring was proposed in [296]. The system developed in [297], uses the Otsu method for leakage recognition and an algorithm based on the features of the local image grid is used to recognize cracks. A general survey on existing robotic tunnel inspection systems can be found in [298].…”

Section: Tinspect : Vision Based Change Detection System For Inspectionmentioning

confidence: 99%

A novel robotic framework for safe inspection and telemanipulation in hazardous and unstructured environments

Castro¹

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First of all, I would like to express my deep gratitude to my research university supervisor, Professor Manuel Ferre Pérez, for his patient guidance, enthusiastic encouragement and useful critiques of this thesis. An immense thankfulness goes to my research CERN supervisor, Doctor Alessandro Masi, who has believed in my potential and who has always supported me during the last years, being my mentor and my inspiration. Thanks to the unconditional support of Roberto Losito for the direction of the Engineering Department at CERN, that provides extraordinary opportunities and individual development to its scientists. I would like to express my great appreciation and deep respect to Professor Raúl Marín Prades for the support for writing of the thesis, help and profound discussions on teleoperation and robotic controls. Also, I would like to offer my special thanks to my colleagues of the Mechatronics, Robotics and Operation Section within the Survey, Mechatronics and Measurement Group at CERN for their inestimable help in the building-up of this work.

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Inspection equipment study for subway tunnel defects by grey-scale image processing

Cited by 122 publications

References 14 publications

A Modified Adaptive Thresholding Method using Cuckoo Search Algorithm for Detecting Surface Defects

A Modified Adaptive Thresholding Method using Cuckoo Search Algorithm for Detecting Surface Defects

A New Health Assessment Index of Tunnel Lining Based on the Digital Inspection of Surface Cracks

A novel robotic framework for safe inspection and telemanipulation in hazardous and unstructured environments

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