Optimized minimal path selection (OMPS) method for automatic and unsupervised crack segmentation within two-dimensional pavement images

Kaddah, Wissam; Elbouz, Marwa; Ouerhani, Yousri; Baltazart, Vincent; Desthieux, Marc; Alfalou, Ayman

doi:10.1007/s00371-018-1515-9

Cited by 32 publications

(19 citation statements)

References 21 publications

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“…Faster computation may be achieved by combining more recent computer with the latest GPU technology. Ongoing work in [27] Fig. 4.…”

Section: Discussionmentioning

confidence: 96%

“…Fig. 1) consists in refining the crack skeleton and estimating the crack width along the crack skeleton (see [1,27] for details).…”

Section: General Synoptic Of Mpsmentioning

confidence: 99%

“…Then, MPS-V1 displays a crack skeleton which is closer to the PGT. The performances assessment which is conducted over the full size pavement images in [27] has shown that MPS -V1 reach a larger DSC rate than the original MPS version in [1].…”

Section: Illustrative Examplementioning

confidence: 99%

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Ongoing tests and improvements of the MPS algorithm for the automatic crack detection within grey level pavement images

Baltazart¹,

Nicolle²,

Yang

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-The MPS approach (Minimal Path Selection) has shown in [1] to provide robust and accurate segmentation of cracks within pavement images compared to other algorithms. As a counterpart, MPS suffers from a large computing time. In this paper, we present three different ongoing improvements to reduce the computing time and to improve the overall segmentation performance. Most of the work focuses on the first three steps of the algorithm which achieve the segmentation of the crack skeleton. This is at first the improvement of the MPS methodology under Matlab coding, then, the C language MPS version and finally, the first attempt to parallelize MPS under the GPU platform. The results on pavement images illustrate the achieved improvements in terms of better segmentation and faster computational time.

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“…Faster computation may be achieved by combining more recent computer with the latest GPU technology. Ongoing work in [27] Fig. 4.…”

Section: Discussionmentioning

confidence: 96%

“…Fig. 1) consists in refining the crack skeleton and estimating the crack width along the crack skeleton (see [1,27] for details).…”

Section: General Synoptic Of Mpsmentioning

confidence: 99%

See 1 more Smart Citation

Ongoing tests and improvements of the MPS algorithm for the automatic crack detection within grey level pavement images

Baltazart¹,

Nicolle²,

Yang

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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show abstract

“…The latter solution may be worth pending further progress on automatic crack detection algorithms. The ongoing improvements in [22][23] on MPS do not achieve yet the top DSC rate (discussed in section VI-B) to refrain from the PGT data collection.…”

Section: Discussionmentioning

confidence: 99%

Pseudo-ground truth data collection on pavement images

Baltazart¹,

Yang²,

Nicolle³

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-The performance assessment of automatic crack detection algorithms within pavement images requires beforehand to establish a reference image, namely, the pseudoground truth image (PGT). In this context, this paper presents some existing pseudo-ground truth (PGT) data collection techniques which rely on image processing techniques. The processing of five Single Pair Shortest Path (SPSP) algorithms which are devoted to this aim are illustrated in terms of running time and segmentation accuracy on a pavement image.

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“…e most easily observable form of pavement deterioration is cracks. Cracks are widely considered to be the most important indicator of pavement condition because this type of distress directly affects in pavement serviceability and structural integrity [5,6].…”

Section: Introductionmentioning

confidence: 99%

Fast Local Laplacian‐Based Steerable and Sobel Filters Integrated with Adaptive Boosting Classification Tree for Automatic Recognition of Asphalt Pavement Cracks

Hoang

Nguyen

2018

Advances in Civil Engineering

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Effective road maintenance requires adequate periodic surveys of asphalt pavement condition. The manual process of pavement assessment is labor intensive and time-consuming. This study proposes an alternative for automating the periodic surveys of pavement condition by means of image processing and machine learning. Advanced image processing techniques including fast local Laplacian filter, Sobel filter, steerable filter, and projection integral are employed for image enhancement and analysis to extract useful features from digital images. Based on the features produced by these image processing techniques, adaptive boosting classification tree is used to perform pavement crack recognition tasks. A dataset of image samples consisting of five classes (alligator crack, diagonal crack, longitudinal crack, noncrack, and transverse crack) has been collected to construct and verify the performance of the adaptive boosting classification tree. The experimental results show that the proposed approach has achieved a high crack classification accuracy which is roughly 90%. Therefore, the newly developed model is a promising alternative to help transportation agencies in pavement condition evaluation.

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Optimized minimal path selection (OMPS) method for automatic and unsupervised crack segmentation within two-dimensional pavement images

Cited by 32 publications

References 21 publications

Ongoing tests and improvements of the MPS algorithm for the automatic crack detection within grey level pavement images

Ongoing tests and improvements of the MPS algorithm for the automatic crack detection within grey level pavement images

Pseudo-ground truth data collection on pavement images

Fast Local Laplacian‐Based Steerable and Sobel Filters Integrated with Adaptive Boosting Classification Tree for Automatic Recognition of Asphalt Pavement Cracks

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