Segmentation of non-stochastic surfaces based on non-subsampled contourlet transform and mathematical morphologies

Li, Linfu; Zhang, Xiangchao; Xiao, Hong; Xu, Min

doi:10.1016/j.measurement.2015.08.014

Cited by 10 publications

(1 citation statement)

References 19 publications

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“…For a fair comparison, four transform‐based methods are taken for the analysis. Wavelet transform‐based method (WT), 47 curvelet transform‐based method (CT), 48 contourlet transform‐based method (COT), 49 and shearlet transform‐based 50 crack detection (ST) methods are taken for the comparative analysis. Figure 18 shows the sample outputs of crack detection for proposed method and state‐of‐the‐art methods.…”

Section: Methodsmentioning

confidence: 99%

A novel approach for thermal crack detection and quantification in structural concrete using ripplet transform

Andrushia

Anand

2020

Struct Control Health Monit

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Fire accidents in the concrete structures affect the safety of human beings and structural components. Due to higher temperatures, thermal cracks are conceived in concrete elements. Crack identification, localization, and quantification are the three major components in the damage assessment system. This paper aims to detect thermal cracks of fire-affected concrete structures using ripplet transform-based computer vision method. Initially, the concrete images are decomposed with discrete ripplet transform (DRT). The low frequency subbands are approximated with column filtering-based gray level difference metric. It removes the uneven concrete background. Ripplet coefficient variance parameter (RCVP) is used to differentiate the crack pixels from noisy pixels which is used to eliminate the noises. Finally, in the reconstructed image, the major and minor thermal cracks are detected. The obtained thermal cracks are quantified with the crack properties of length, width, area, and perimeter. The novelty of the proposed method relays on the usage of ripplet transform for the detection of thermal cracks for different concrete grades and different durations of temperature profile. The experimental results are compared with four transform domain state-of-the-method crack detection methods. The proposed method yields better results in terms of the accuracy and average execution time. K E Y W O R D S crack detection, crack properties, discrete ripplet transform, temperature, thermal crack 1 | INTRODUCTION Many automatic methods are being developed to monitor the structural health. The automatic methods are developed with the objective of reducing the manpower requirement and to perform effective analysis, so as to minimize the human errors in the concrete structures. Periodical evaluation is done through automatic systems to identify the damage of concrete structures. Assessment of damages in various infrastructure is possible through structural health monitoring (SHM) approaches. 1,2 Cracks are present in all type of concrete structures. Concrete structures are often affected by stress, environmental factors, heavy loading, and aging. In extreme cases, the damages to concrete structures may result in loss of the life of occupants. Identification and quantification of cracks are paramount to evaluate the present conditions of structures and to take necessary actions for retrofitting the structures.

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Section: Methodsmentioning

confidence: 99%

A novel approach for thermal crack detection and quantification in structural concrete using ripplet transform

Andrushia

Anand

2020

Struct Control Health Monit

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LSA-Net: Location and shape attention network for automatic surface defect segmentation

Liu

Niu

et al. 2023

Journal of Manufacturing Processes

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Evaluation of thermal cracks on fire exposed concrete structures using Ripplet transform

Andrushia

Anand

Arulraj

2021

Mathematics and Computers in Simulation

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Segmentation of non-stochastic surfaces based on non-subsampled contourlet transform and mathematical morphologies

Cited by 10 publications

References 19 publications

A novel approach for thermal crack detection and quantification in structural concrete using ripplet transform

A novel approach for thermal crack detection and quantification in structural concrete using ripplet transform

LSA-Net: Location and shape attention network for automatic surface defect segmentation

Evaluation of thermal cracks on fire exposed concrete structures using Ripplet transform

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Segmentation of non-stochastic surfaces based on non-subsampled contourlet transform and mathematical morphologies

Cited by 10 publications

References 19 publications

A novel approach for thermal crack detection and quantification in structural concrete using ripplet transform

A novel approach for thermal crack detection and quantification in structural concrete using ripplet transform

LSA-Net: Location and shape attention network for automatic surface defect segmentation

Evaluation of thermal cracks on fire exposed concrete structures using Ripplet​ transform

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Evaluation of thermal cracks on fire exposed concrete structures using Ripplet transform