A Survey On Road Crack Detection Techniques

Bhat, Sharmad; Naik, Saish; Gaonkar, Mandar; Sawant, Pradnya; Aswale, Shailendra; Shetgaonkar, Pratiksha

doi:10.1109/ic-etite47903.2020.67

Cited by 26 publications

(12 citation statements)

References 18 publications

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“…While this tolerant approach is sufficient for counting and locating cracks, it is not for measuring their width -an indicator of the cracks' age and severity [8].…”

Section: Pixelaccuratementioning

confidence: 99%

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Pixel-accurate road crack detection in presence of inaccurate annotations

2022

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“…While this tolerant approach is sufficient for counting and locating cracks, it is not for measuring their width -an indicator of the cracks' age and severity [8].…”

Section: Pixelaccuratementioning

confidence: 99%

“…In addition to that, there is an interest to measure the cracks width, which is one of the indicators of the severity of the damage and future durability of many constructions, such as concrete structures [2]. Measuring the width requires a pixel-accurate crack detection [3,4,5,6,7], which is still a challenging task [8].…”

Section: Introductionmentioning

confidence: 99%

Pixel-accurate road crack detection in presence of inaccurate annotations

2022

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“…The presence of noise, shadows, blemishes and other disturbances in the images is a problem commonly faced by researchers in using both image processing and machine learning methods [5,30]. Hence, more research needs to be conducted to develop methods that can remove noise and other irregularities from images [23,71].…”

Section: Gaps and Challengesmentioning

confidence: 99%

Image-Based Crack Detection Methods: A Review

et al. 2021

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Annually, millions of dollars are spent to carry out defect detection in key infrastructure including roads, bridges, and buildings. The aftermath of natural disasters like floods and earthquakes leads to severe damage to the urban infrastructure. Maintenance operations that follow for the damaged infrastructure often involve a visual inspection and assessment of their state to ensure their functional and physical integrity. Such damage may appear in the form of minor or major cracks, which gradually spread, leading to ultimate collapse or destruction of the structure. Crack detection is a very laborious task if performed via manual visual inspection. Many infrastructure elements need to be checked regularly and it is therefore not feasible as it will require significant human resources. This may also result in cases where cracks go undetected. A need, therefore, exists for performing automatic defect detection in infrastructure to ensure its effectiveness and reliability. Using image processing techniques, the captured or scanned images of the infrastructure parts can be analyzed to identify any possible defects. Apart from image processing, machine learning methods are being increasingly applied to ensure better performance outcomes and robustness in crack detection. This paper provides a review of image-based crack detection techniques which implement image processing and/or machine learning. A total of 30 research articles have been collected for the review which is published in top tier journals and conferences in the past decade. A comprehensive analysis and comparison of these methods are performed to highlight the most promising automated approaches for crack detection.

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“…For many constructions, such as roads (Coquelle et al, 2012) or concrete structures (Yang et al, 2018), the cracks' width is one of the indicators of the damage severity and future durability. Measuring the width requires a pixel-accurate crack detection (Escalona et al, 2019;Gao et al, 2019;Sun et al, 2020;Yang et al, 2020), which is still a challenging task (Bhat et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Syncrack: Improving Pavement and Concrete Crack Detection through Synthetic Data Generation

Rill-García

Dokladalova

Dokládal

2022

Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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In crack detection, pixel-accurate predictions are necessary to measure the width -an important indicator of the severity of a crack. However, manual annotation of images to train supervised models is a hard and time-consuming task. Because of this, manual annotations tend to be inaccurate, particularly at pixel-accurate level. The learning bias introduced by this inaccuracy hinders pixel-accurate crack detection. In this paper we propose a novel tool aimed for synthetic image generation with accurate crack labels -Syncrack. This parametrizable tool also provides a method to introduce controlled noise to annotations, emulating human inaccuracy. By using this, first we do a robustness study of the impact of training with inaccurate labels. This study quantifies the detrimental effect of inaccurate annotations in the final prediction scores. Afterwards, we propose to use Syncrack to avoid this detrimental effect in a real-life context. For this, we show the advantages of using Syncrack generated images with accurate annotations for crack detection on real road images. Since supervised scores are biased by the inaccuracy of annotations, we propose a set of unsupervised metrics to evaluate the segmentation quality in terms of crack width. a https://orcid.

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A Survey On Road Crack Detection Techniques

Cited by 26 publications

References 18 publications

Pixel-accurate road crack detection in presence of inaccurate annotations

Pixel-accurate road crack detection in presence of inaccurate annotations

Image-Based Crack Detection Methods: A Review

Syncrack: Improving Pavement and Concrete Crack Detection through Synthetic Data Generation

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