Asphalt surfaced pavement cracks detection based on histograms of oriented gradients

Kapela, Rafał; Śniatała, Pawel; Turkot, Adam; Rybarczyk, Agnieszka; Pożarycki, Andrzej; Rydzewski, Paweł; Wyczałek, Michał; Bloch, Adam

doi:10.1109/mixdes.2015.7208590

Cited by 87 publications

(34 citation statements)

References 5 publications

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“…Both systems consist of several laser based measurement devices for texture analysis and range finding, as well as of several high speed cameras. It is worth mentioning that in all works, cameras are placed orthogonally downward, facing the road pavement [12,[51][52][53]. Moreover, in most cases, surface cameras are synchronized with a high performance lighting unit that makes the system independent of exterior lighting conditions and shadows cast by various roadside objects and allows working with different types of pavement, from concrete to dark asphalt, one lane at a time.…”

Section: Source Input Data and Data Collectionmentioning

confidence: 99%

Pavement Distress Detection with Deep Learning Using the Orthoframes Acquired by a Mobile Mapping System

et al. 2019

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The subject matter of this research article is automatic detection of pavement distress on highway roads using computer vision algorithms. Specifically, deep learning convolutional neural network models are employed towards the implementation of the detector. Source data for training the detector come in the form of orthoframes acquired by a mobile mapping system. Compared to our previous work, the orthoframes are generally of better quality, but more importantly, in this work, we introduce a manual preprocessing step: sets of orthoframes are carefully selected for training and manually digitized to ensure adequate performance of the detector. Pretrained convolutional neural networks are then fine-tuned for the problem of pavement distress detection. Corresponding experimental results are provided and analyzed and indicate a successful implementation of the detector.

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Section: Source Input Data and Data Collectionmentioning

confidence: 99%

Pavement Distress Detection with Deep Learning Using the Orthoframes Acquired by a Mobile Mapping System

et al. 2019

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“…The neighbor of the pixel can be used to form a texture/shape pixel pattern. In [18], histograms of oriented gradients (HoG) are used to build the pixel pattern, which is classified via SVM.…”

Section: Related Workmentioning

confidence: 99%

Automatic crack detection on concrete floor images

Simler

Trostmann

Berndt

2019

Photonics and Education in Measurement Science 2019

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This paper presents an algorithm detecting automatically a wide variety of cracks on monochrome images of concrete floors. It is part of a vision system, generating a crack map to support the condition monitoring for buildings. The suggested method uses successively radiometric, geometric and contextual information. An automatic supervised adaptive intensity-threshold method handles radiometry information. A threshold-based method was chosen because it can separate even thin cracks from the background. In order to cope with different image cluster intensities, first a clustering algorithm separates regions of different intensities, and second the threshold is adaptive at the cluster level; being a continuous function of the cluster intensity. At some points, function values were learned via supervised classification. Then, we performed an interpolation between these points in order to get a threshold whatever the cluster intensity (continuity). At this step, we have a thresholded image. However, due to texture, non-crack dark defects and dirtiness we have false positives. In order to overcome this problem, connected pixels are grouped into regions, and after discarding small regions, a size-dependent geometrical shape filter is suggested. The shape of a crack region depends on its area, and this relation was empirically learned. A region is retained if its shape features are above some area depending thresholds. However, mainly due to texture, we have still false positives because some non-crack entities have radiometry and geometry of cracks. Fortunately, they are often small isolated regions and are discarded via an isolation filter. Tests performed on many images show very encouraging results.

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“…Challenges of Bangkok. From Bangkokian perspective whose daily life suffers from the world most severe evening rush hour traffic (ranked by CNN Money in February 2017), it is preferable not to introduce any more traffic jam by blocking roads or lanes in order to run a huge full-scale automatic road distress analysis system/vehicle as proposed in [14,15]. Although that kind of systems produce better and more reliable results, its ease of use is not practical in real life and very difficult to perform inspection frequently.…”

Section: Proposed Systemmentioning

confidence: 99%

An Automatic Road Distress Visual Inspection System Using an Onboard In-Car Camera

Siriborvornratanakul

2018

Advances in Multimedia

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Speaking of road maintenance, the preventive maintenance strategy is preferable for most governments. Many governments possess special vehicles that can accurately detect and classify many types of road distresses. By running these vehicles frequently, small road distresses will be detected before growing into the big ones. However, because running these huge and expensive vehicles is not easy, in practical, it usually ends up with infrequent road inspection regardless of having automatic road inspection vehicles. In this paper, we focus on investigating and developing an automatic and nondestructive visual inspection system whose setup and usage are designed by considering the context of drivers, driving styles, and road conditions in Bangkok, the capital city of Thailand. Our proposal includes a workflow diagram of a vision-based road inspection system that is capable of detecting, classifying, tracking, measuring, and pricing road distresses. As for the proof-of-concept, our current system focuses on detecting one specific type of road distresses called pothole, using only one onboard in-car camera. Experimental results reveal that the context of Bangkok introduces many nontrivial challenges for vision-based analysis systems where maintaining both accuracy and ease of use altogether may not be easy.

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Asphalt surfaced pavement cracks detection based on histograms of oriented gradients

Cited by 87 publications

References 5 publications

Pavement Distress Detection with Deep Learning Using the Orthoframes Acquired by a Mobile Mapping System

Pavement Distress Detection with Deep Learning Using the Orthoframes Acquired by a Mobile Mapping System

Automatic crack detection on concrete floor images

An Automatic Road Distress Visual Inspection System Using an Onboard In-Car Camera

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