Pavement pathologies classification using graph-based features

Fernandes, Kelwin; Ciobanu, Lucian

doi:10.1109/icip.2014.7025159

Cited by 32 publications

(13 citation statements)

References 9 publications

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“…However, the performance of these methods is usually dependent upon the parameter choices [13] which are very difficult to accomplish for field images with significant visual clutters, leading to unreliable detection results in airport runway inspection applications. Machine learning based crack detection methods build on techniques such as support vector machines [14], random forest [15], random structured forest [16], and neural networks [17]. The machine learning based methods obtain more robust performance compared with image processing techniques.…”

Section: Related Workmentioning

confidence: 99%

Automated Defect Detection and Visualization for the Robotic Airport Runway Inspection

Gui

2020

IEEE Access

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Detection of both surface and subsurface defects is a vital task for maintaining the structural health and reliability of airport runways. We report the automated data collection and analysis for airport runways based on our novel robotic system, which employs a camera and a GPR (Ground Penetrating Radar) to inspect the surface and subsurface conditions, respectively. To perform the automated data analysis, we propose a novel crack detection algorithm based on the images, and a subsurface defect detection method with GPR data. Additionally, to create a composite global view of a large airport runway span, a camera/GPR data sequence from the robot is aligned accurately to create a continuous mosaic for visualization. We combine these algorithms into a software to perform automated on-site analysis. We have put our robot and software into engineering practice over 20 airports in China, achieving the performance of 70% and 67% F1-measure for crack detection and subsurface defect detection, respectively. More importantly, the results of our algorithms can satisfy the requirement of applications.

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Section: Related Workmentioning

confidence: 99%

Automated Defect Detection and Visualization for the Robotic Airport Runway Inspection

Gui

2020

IEEE Access

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“…One promising solution method for crack detection is to utilize supervised learning for crack prediction. These methods include support vector machines (SVMs) [20][21], artificial neural networks [22], the hybrid chromosome genetic al- gorithm [23], CrackIT [24], CrackBT [25], sketch tokens [26], and CrackForest [27][28]. Exploiting effective feature descriptors, such as texture features [29], standard deviation, and mean parameters [30], is necessary to distinguishes crack blocks from non-crack blocks.…”

Section: B Supervised Learning-based Approachmentioning

confidence: 99%

FCN-SFW: Steel Structure Crack Segmentation Using a Fully Convolutional Network and Structured Forests

et al. 2020

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Tiny cracks that exist in steel beams have poor continuity and low contrast in images, posing a huge challenge to crack detection using image-based approaches. When complex backgrounds exist, the existing deep learning methods are usually unable to perform effective feature transfer and fusion for crack feature mapping, and they cannot accurately distinguish crack features from similar backgrounds. In this article, we propose a fusion segmentation algorithm, using the fully convolutional network (FCN) and structured forests with wavelet transform (SFW) to detect tiny cracks in steel beams. First, five neural networks based on the FCN framework are constructed to extend the global characteristics of tiny cracks. Second, a fine edge detection approach using multi-scale structured forests and wavelet maximum modulus edge detection to refine the characteristics of tiny cracks are proposed. Here, a competitive training strategy is used to address the SFW parameter optimization problem. Finally, we fuse the multiple probability maps, acquired from both the optimal FCN model and the SFW classifier, into a merged map, which can segment tiny cracks with robustness better than the comparison approaches. The experimental results show that compared with state-of-the-art algorithms and other segmentation approaches, the proposed algorithm realizes better segmentation in terms of quantitative metrics. INDEX TERMS Steel structure crack segmentation, fully convolutional network, structured forests, wavelet transform, maximum modulus edge detection.

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“…ese methods lack the description of global information and are sensitive to noise. To improve the continuity of crack detection, researchers have attempted to detect cracks by introducing minimal path selection (MPS) [8][9][10], minimal spanning trees (MSTs) [11][12][13], and crack fundamental elements (CFEs) [14]. ese methods can partially eliminate noise and improve crack detection continuity.…”

Section: Introductionmentioning

confidence: 99%

Automated Pavement Crack Damage Detection Using Deep Multiscale Convolutional Features

Song

Jia

Zhu

et al. 2020

Journal of Advanced Transportation

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Road pavement cracks automated detection is one of the key factors to evaluate the road distress quality, and it is a difficult issue for the construction of intelligent maintenance systems. However, pavement cracks automated detection has been a challenging task, including strong nonuniformity, complex topology, and strong noise-like problems in the crack images, and so on. To address these challenges, we propose the CrackSeg—an end-to-end trainable deep convolutional neural network for pavement crack detection, which is effective in achieving pixel-level, and automated detection via high-level features. In this work, we introduce a novel multiscale dilated convolutional module that can learn rich deep convolutional features, making the crack features acquired under a complex background more discriminant. Moreover, in the upsampling module process, the high spatial resolution features of the shallow network are fused to obtain more refined pixel-level pavement crack detection results. We train and evaluate the CrackSeg net on our CrackDataset, the experimental results prove that the CrackSeg achieves high performance with a precision of 98.00%, recall of 97.85%, F-score of 97.92%, and a mIoU of 73.53%. Compared with other state-of-the-art methods, the CrackSeg performs more efficiently, and robustly for automated pavement crack detection.

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Pavement pathologies classification using graph-based features

Cited by 32 publications

References 9 publications

Automated Defect Detection and Visualization for the Robotic Airport Runway Inspection

Automated Defect Detection and Visualization for the Robotic Airport Runway Inspection

FCN-SFW: Steel Structure Crack Segmentation Using a Fully Convolutional Network and Structured Forests

Automated Pavement Crack Damage Detection Using Deep Multiscale Convolutional Features

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