Automatic Asphalt pavement crack detection and classification using Neural Networks

Saar, T.; Talvik, Ott

doi:10.1109/bec.2010.5630750

Cited by 54 publications

(23 citation statements)

References 7 publications

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“…This classification has been carried out only on areas with coarse texture to know more about local structure. More work carried out on the same technique by training artificial neural networks and wavelet transformation for pavement distress image compression noise reduction and evaluation [6] [7].…”

Section: Literature Reviewmentioning

confidence: 99%

Pavement crack detection using the Gabor filter

Salman

Mathavan

Kamal

et al. 2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

210

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Crack is a common form of pavement distress and it carries significant information on the condition of roads. The detection of cracks is essential to perform pavement maintenance and rehabilitation. Many of the highways agencies, in different countries, are still employing conventional, costly and very time consuming techniques which involve direct human intervention and assessment. Although automated recognition has been successfully performed for many pavement distresses, crack detection remains, to this date, a topic where reservations exist. A novel approach to automatically distinguish cracks in digital pavement images is proposed in this paper. The Gabor filter is proven to be a highly potential technique for multidirectional crack detection that was not done previously using the Gabor filter. Image analysis using the Gabor function is directly related to the mammalian visual perception, hence the choice of this method for crack detection. Results reported in this paper concentrate on pavement images with high levels of surface texture that makes crack detection difficult. An initial detection precision of up to 95% has been reported in this paper showing a good promise in the proposed method.

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Section: Literature Reviewmentioning

confidence: 99%

Pavement crack detection using the Gabor filter

Salman

Mathavan

Kamal

et al. 2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

210

View full text Add to dashboard Cite

show abstract

“…Classification has been carried out only on areas with coarse texture to know more about local structure. Training artificial neural networks for pavement distress image compression, noise reduction and evaluation led to further studies and activities on the same technique [7][8]. Chambon et al [9] introduced a wavelet and 2D matched filter in order to define an adapted wavelet and then used the results of this multi scale detection into a Markov Random Field (MRF) process to segment fine structures of images for road crack detection.…”

Section: Literature Reviewmentioning

confidence: 99%

Tiled fuzzy Hough transform for crack detection

Vaheesan¹,

Chandrakumar

Mathavan

et al. 2015

Twelfth International Conference on Quality Control by Artificial Vision 2015

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Surface cracks can be the bellwether of the failure of any component under loading as it indicates the component's fracture due to stresses and usage. For this reason, crack detection is indispensable for the condition monitoring and quality control of road surfaces. Pavement images have high levels of intensity variation and texture content, hence the crack detection is difficult. Moreover, shallow cracks result in very low contrast image pixels making their detection difficult. For these reasons, studies on pavement crack detection is active even after years of research. In this paper, the fuzzy Hough transform is employed, for the first time to detect cracks on any surface. The contribution of texture pixels to the accumulator array is reduced by using the tiled version of the Hough transform. Precision values of 78% and a recall of 72% are obtaining for an image set obtained from an industrial imaging system containing very low contrast cracking. When only high contrast crack segments are considered the values move to mid to high 90%.

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“…This is due to the fact that pavement cracks are seen as the most predominant distress type [45] and they are also easier to measure, with the typical requirements being simply to measure the crack's width and length. There are a tremendous number of studies on developing specific neural networks for crack detection and analysis using both 2D and 3D imagery [41,42,[46][47][48][49][50][51][52] and with comparisons made to results from image-based toolboxes for crack detection and analysis such as CrackIT [53]. While the detection and monitoring of cracks are important to road agencies, this represents only one main category of distress.…”

Section: The Use Of Deep Learning In Pavement Engineeringmentioning

confidence: 99%

Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning

et al. 2020

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Governments are faced with countless challenges to maintain conditions of road networks. This is due to financial and physical resource deficiencies of road authorities. Therefore, low-cost automated systems are sought after to alleviate these issues and deliver adequate road conditions for citizens. There have been several attempts at creating such systems and integrating them within Pavement management systems. This paper utilizes replicable deep learning techniques to carry out hotspot analyses on urban road networks highlighting important pavement distress types and associated severities. Following this, analyses were performed illustrating how the hotspot analysis can be carried out to continuously monitor the structural health of the pavement network. The methodology is applied to a road network in Sicily, Italy where there are numerous roads in need of rehabilitation and repair. Damage detection models were created which accurately highlight the location and a severity assessment. Harmonized distress categories, based on industry standards, are utilized to create practical workflows. This creates a pipeline for future applications of automated pavement distress classification and a platform for an integrated approach towards optimizing urban pavement management systems.

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Automatic Asphalt pavement crack detection and classification using Neural Networks

Cited by 54 publications

References 7 publications

Pavement crack detection using the Gabor filter

Pavement crack detection using the Gabor filter

Tiled fuzzy Hough transform for crack detection

Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning

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