Image-Based Pothole Detection System for ITS Service and Road Management System

Ryu, Seung-Ki; Kim, Tae-Hyeong; Kim, Young-Ro

doi:10.1155/2015/968361

Cited by 120 publications

(53 citation statements)

References 12 publications

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“…Nevertheless, our proposed system is different from most existing works [2,4,[10][11][12][13]. The work of [10] does not focus on solving the problems of road distresses, particularly in the context of Bangkok as our paper does whereas the work of [4] is based on unmanned aerial vehicles not on-road vehicles like ours.…”

Section: Related Workmentioning

confidence: 91%

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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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Section: Related Workmentioning

confidence: 91%

“…Ryu et al [12] built an image-based pothole detection system based on 2D images. This work was expected to help ITS (Intelligent Transportation System) service and road management system.…”

Section: Related Workmentioning

confidence: 99%

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

Siriborvornratanakul

2018

Advances in Multimedia

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“…where TP (True Positive) is a class of objects that are correctly classified, TN (true negative) is another class of objects that are correctly classified, FP (false positive) is a class of objects that are not correctly classified, while FN (false negative) is another class of objects that are not properly classified [10].…”

Section: Accuracy = (Tp + Tn) / (Tp + Tn + Fp + Fn)mentioning

confidence: 99%

Pavement Distress Classification Using Deep Learning Method Based on Digital Image

Sulistyaningrum¹,

Oranova²,

Pramestya³

et al. 2020

Proceedings of the 2nd International Symposium on Transportation Studies in Developing Countries (ISTSDC 2019)

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Maintaining the road regularly is a necessity, because the road is a vital infrastructure. One of automatic road maintenance steps is the detection of road distress type. Several methods have been used to detect and classify road distress automatically. This research determines the existence and classifies the deterioration of pavement using the Deep Learning method. The type of road distress detected are potholes, line-cracks, and non-line cracks. In this study, the deep learning method implemented is You Only Look Once (YOLO). The YOLO method uses Convolutional Neural Network (CNN) in its architecture and has given good results in object detection both on images and videos. YOLO has been tested in various datasets and given faster and accurate results. In this research, the pre-processing steps are cropping and resizing images then annotating the data. After that, the training is done by fine-tuning YOLO network process. The YOLO architecture uses 9-layer convolution and six layer maxpool. The testing results for datasets show that the highest accuracy is 99% and the highest average IoU is 75,1%. The run time of classification is 0.883 seconds per image.

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“…Generally, structure aging, heavy traffic condition, poor drainage, thin asphalt layer substructure, and weak substructure can be the causes of pothole appearance [6]. In developing countries, the pavement pothole is often detected manually by inspectors of local transportation agencies during periodical field surveys.…”

Section: Introductionmentioning

confidence: 99%

“…Lokeshwor et al [15] proposed an adaptive thresholding technique for segmenting distress pixels from the background pixels. e histogram shapebased thresholding and maximum entropy method have been employed in the previous works of Ryu et al [6]. Ouma and Hahn [16] rely on wavelet transform and fuzzy c-means clustering to separate defect and nondefect pavement pixels.…”

Section: Introductionmentioning

confidence: 99%

An Artificial Intelligence Method for Asphalt Pavement Pothole Detection Using Least Squares Support Vector Machine and Neural Network with Steerable Filter‐Based Feature Extraction

Hoang

2018

Advances in Civil Engineering

111

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is study establishes an artificial intelligence (AI) model for detecting pothole on asphalt pavement surface. Image processing methods including Gaussian filter, steerable filter, and integral projection are utilized for extracting features from digital images. A data set consisting of 200 image samples has been collected to train and validate the predictive performance of two machine learning algorithms including the least squares support vector machine (LS-SVM) and the artificial neural network (ANN). Experimental results obtained from a repeated subsampling process with 20 runs show that both LS-SVM and ANN are capable methods for pothole detection with classification accuracy rate larger than 85%. In addition, the LS-SVM has achieved the highest classification accuracy rate (roughly 89%) and the area under the curve (0.96). Accordingly, the proposed AI approach used with LS-SVM can be very potential to assist transportation agencies and road inspectors in the task of pavement pothole detection.

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Image-Based Pothole Detection System for ITS Service and Road Management System

Cited by 120 publications

References 12 publications

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

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

Pavement Distress Classification Using Deep Learning Method Based on Digital Image

An Artificial Intelligence Method for Asphalt Pavement Pothole Detection Using Least Squares Support Vector Machine and Neural Network with Steerable Filter‐Based Feature Extraction

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