Mathematical morphology for TOFD image analysis and automatic crack detection

Merazi-Meksen, Thouraya; Boudraa, Malika; Boudraa, Bachir

doi:10.1016/j.ultras.2014.03.005

Cited by 68 publications

(20 citation statements)

References 11 publications

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“…Due to the concrete-based composition of the majority of civil infrastructures (e.g., dams, roads, buildings, sewers, bridges, tunnels), the techniques developed for concrete crack detection within a particular type of concrete structure can also be generalized towards other civil infrastructures. Some of the earlier works focused on the utilization of basic-level image-processing techniques for crack detection in concrete structures [48,51,88], which included basic-level morphological approaches [166,[190][191][192][193], digital image correlation techniques [194][195][196][197] and different segmentation-based approaches [198]. A number of different image-based filtering techniques were also employed, namely Gabor filtering [199], median filtering [184,200], texture filtering [201,202] and data fusion-based filtering approaches [176,203].…”

Section: Surface-level Analysis: Concrete Crack Detectionmentioning

confidence: 99%

“…The visual inspection of infrastructure is important to provide information regarding the surface-level defects and damages in concrete. A number of vision sensors have been utilized to perform the SHM of civil infrastructures, namely the smartphone camera [ 154 ], digital cameras [ 8 , 88 , 89 , 99 , 152 , 165 ], depth sensors [ 42 , 164 ], time-of-flight cameras [ 166 ], closed-circuit television (CCTV) [ 151 ], laser-scanners [ 77 , 98 , 157 , 164 ] and Visual SONAR (Sound Navigation and Ranging) sensor [ 41 ]. The vision-based sensors are dependent on the reflection of light from infrastructure surfaces to provide an assessment of surface-level NDE.…”

Section: Tools and Techniques For Data Collectionmentioning

confidence: 99%

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Review of Non-Destructive Civil Infrastructure Evaluation for Bridges: State-of-the-Art Robotic Platforms, Sensors and Algorithms

Ahmed

Gucunski

2020

Sensors

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The non-destructive evaluation (NDE) of civil infrastructure has been an active area of research in recent decades. The traditional inspection of civil infrastructure mostly relies on visual inspection using human inspectors. To facilitate this process, different sensors for data collection and techniques for data analyses have been used to effectively carry out this task in an automated fashion. This review-based study will examine some of the recent developments in the field of autonomous robotic platforms for NDE and the structural health monitoring (SHM) of bridges. Some of the salient features of this review-based study will be discussed in the light of the existing surveys and reviews that have been published in the recent past, which will enable the clarification regarding the novelty of the present review-based study. The review methodology will be discussed in sufficient depth, which will provide insights regarding some of the primary aspects of the review methodology followed by this review-based study. In order to provide an in-depth examination of the state-of-the-art, the current research will examine the three major research streams. The first stream relates to technological robotic platforms developed for NDE of bridges. The second stream of literature examines myriad sensors used for the development of robotic platforms for the NDE of bridges. The third stream of literature highlights different algorithms for the surface- and sub-surface-level analysis of bridges that have been developed by studies in the past. A number of challenges towards the development of robotic platforms have also been discussed.

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Section: Surface-level Analysis: Concrete Crack Detectionmentioning

confidence: 99%

Section: Tools and Techniques For Data Collectionmentioning

confidence: 99%

Review of Non-Destructive Civil Infrastructure Evaluation for Bridges: State-of-the-Art Robotic Platforms, Sensors and Algorithms

Ahmed

Gucunski

2020

Sensors

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“…M. Salman et al [22] developed a novel technology for the automatic detection and identification of cracks from digital pavement images. Merazi-Meksen et al [23] used a mathematical method to extract the discontinuity-related pixels and used pattern recognition technology to characterize the discontinuity. Due to the complexity of the texture of pavement surfaces, and the irregularity of the crack morphologies, scholars tried to detect the cracks with machine learningbased algorithms.…”

Section: Introductionmentioning

confidence: 99%

Attention‐Based Convolutional Neural Network for Pavement Crack Detection

Wan

Gao

et al. 2021

Advances in Materials Science and Engineering

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Achieving high detection accuracy of pavement cracks with complex textures under different lighting conditions is still challenging. In this context, an encoder-decoder network-based architecture named CrackResAttentionNet was proposed in this study, and the position attention module and channel attention module were connected after each encoder to summarize remote contextual information. The experiment results demonstrated that, compared with other popular models (ENet, ExFuse, FCN, LinkNet, SegNet, and UNet), for the public dataset, CrackResAttentionNet with BCE loss function and PRelu activation function achieved the best performance in terms of precision (89.40), mean IoU (71.51), recall (81.09), and F1 (85.04). Meanwhile, for a self-developed dataset (Yantai dataset), CrackResAttentionNet with BCE loss function and PRelu activation function also had better performance in terms of precision (96.17), mean IoU (83.69), recall (93.44), and F1 (94.79). In particular, for the public dataset, the precision of BCE loss and PRelu activation function was improved by 3.21. For the Yantai dataset, the results indicated that the precision was improved by 0.99, the mean IoU was increased by 0.74, the recall was increased by 1.1, and the F1 for BCE loss and PRelu activation function was increased by 1.24.

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“…Salehin et al [19] proposed conic detection by applying Pascal's theorem (i.e., approximating the curve from two tangent lines and a point from the conic). Merazi-Meksen et al [20] detected parabolic forms from Time-Of-Flight Diffraction images in order to analyze material defects. Detection procedure is named randomized Hough transform and is a combination of Least Squares, Randomized Sampled Consensus (RANSAC), and Hough transform.…”

Section: Introductionmentioning

confidence: 99%

Fast Parabola Detection Using Estimation of Distribution Algorithms

Guerrero-Turrubiates

Cruz-Aceves

Ledesma

et al. 2017

Computational and Mathematical Methods in Medicine

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This paper presents a new method based on Estimation of Distribution Algorithms (EDAs) to detect parabolic shapes in synthetic and medical images. The method computes a virtual parabola using three random boundary pixels to calculate the constant values of the generic parabola equation. The resulting parabola is evaluated by matching it with the parabolic shape in the input image by using the Hadamard product as fitness function. This proposed method is evaluated in terms of computational time and compared with two implementations of the generalized Hough transform and RANSAC method for parabola detection. Experimental results show that the proposed method outperforms the comparative methods in terms of execution time about 93.61% on synthetic images and 89% on retinal fundus and human plantar arch images. In addition, experimental results have also shown that the proposed method can be highly suitable for different medical applications.

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Mathematical morphology for TOFD image analysis and automatic crack detection

Cited by 68 publications

References 11 publications

Review of Non-Destructive Civil Infrastructure Evaluation for Bridges: State-of-the-Art Robotic Platforms, Sensors and Algorithms

Review of Non-Destructive Civil Infrastructure Evaluation for Bridges: State-of-the-Art Robotic Platforms, Sensors and Algorithms

Attention‐Based Convolutional Neural Network for Pavement Crack Detection

Fast Parabola Detection Using Estimation of Distribution Algorithms

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