Visual analysis of asphalt pavement for detection and localization of potholes

Yousaf, Muhammad Haroon; Azhar, Kanza; Murtaza, Fiza; Hussain, Fawad

doi:10.1016/j.aei.2018.09.002

Cited by 75 publications

(41 citation statements)

References 12 publications

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“…e results validate that most potholes in cement concrete pavement images can be correctly detected by LIBSVM and that the selected feature value is reliable. In a study by Yousaf et al [2], they computed the scaleinvariant feature transform features and trained and tested features with SVMs.…”

Section: Pothole Detection Resultsmentioning

confidence: 99%

“…However, in reality, because cement concrete pavement must bear various loads and natural factors [1], cement concrete pavement defects arise gradually, severely affecting the functionality of pavement. Pothole is the most common form of distress on cement concrete pavement [2,3] with a minimum dimension of 150 mm [4]. Potholes have significant influences on the running quality of vehicles [5,6], and they can compromise pavement ridability and safety and can even be the cause of major accidents [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…During the detection of pavement distress, the extraction and identification of defects are performed together. Support vector machine (SVM) is a good method for identifying potholes in the case of a small number of samples and complex problems [2,5,31]. Lin and Liu [32] employed texture-based features to detect potholes using SVM classifiers.…”

Section: Introductionmentioning

confidence: 99%

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Detection and Segmentation of Cement Concrete Pavement Pothole Based on Image Processing Technology

Gao

Wang

Zhu

et al. 2020

Mathematical Problems in Engineering

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Potholes are the most common form of distress on cement concrete pavements, which can compromise pavement safety and ridability. Thus, timely and accurate pothole detection is an important task in developing proper maintenance strategies and ensuring driving safety. This paper proposes a method of integrating the processing of grayscale and texture features. This method mainly combines industrial camera to realize rapid and accurate detection of pothole. Image processing techniques including texture filters, image grayscale, morphology, and extraction of the maximum connected domain are used synergistically to extract useful features from digital images. A machine learning model based on the library for support vector machine (LIBSVM) is constructed to distinguish potholes from longitudinal cracks, transverse cracks, and complex cracks. The method is validated using data collected from agricultural and pastoral areas of Inner Mongolia, China. The comprehensive experiments for recognition of potholes show that the recall, precision, and F1-Score achieved are 100%, 97.4%, and 98.7%, respectively. In addition, the overlap rate between the extracted pothole region and the original image is estimated. Images with an overlap rate greater than 90% accounted for 76.8% of the total image, and images with an overlap rate greater than 80% accounted for 94% of the total image. A comparison discloses that the proposed approach is superior to the existing method not only from the perspective of the accuracy of pothole detection but also from the perspective of the segmentation effect and processing efficiency.

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Section: Pothole Detection Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detection and Segmentation of Cement Concrete Pavement Pothole Based on Image Processing Technology

Gao

Wang

Zhu

et al. 2020

Mathematical Problems in Engineering

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“…e automated approach is costly because not only advanced equipment is required, but also subsequent photographs need to be processed. Meanwhile, the equipment is so complicated that road engineers should be trained in advance [25]. In…”

Section: Distress Types Surveymentioning

confidence: 99%

Research on Relationships among Different Distress Types of Asphalt Pavements with Semi‐Rigid Bases in China Using Association Rule Mining: A Statistical Point of View

Jing

Liu

Yang

et al. 2019

Advances in Civil Engineering

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Distress types are significant for asphalt pavement maintenance decision, and relationships among them can greatly influence the decision outcomes. In this study, to analyze the relationships among different distress types from a statistical point of view, 282 asphalt pavements with semirigid base structures in 23 regions of China were surveyed to identify 12 distress types, which were subsequently investigated by association rule mining. Results show that the distress types can be categorized into independent distress types (IDDTs), dependent distress types (DDTs), and rutting secondary distress types (RSDTs) based on the relationships. The relationships among IDDTs are the strongest, and those between IDDTs and DDTs, between rutting and RSDTs, and among depression, pumping, and raveling are also strong, while the others are weak. The weak relationships should be ignored, while the strong ones should be considered to reduce cost and guarantee accuracy of maintenance decision. The IDDTs are the most important distress types, so preventing them from occurring or maintaining them immediately can greatly preserve good performance of asphalt pavements. To help analyze the relationships, a distress-type system was established and verified. In conclusion, this work provides new insights to understand distress types.

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“…Localization. Several researches locate the defectincluded bounding box in the identified image[28,60,61], requiring further processing to gain their positions in global coordination. Additional devices…”

mentioning

confidence: 99%

Computer Vision Techniques in Construction, Operation and Maintenance Phases of Civil Assets: A Critical Review

Xu¹,

Wang²,

Wang³

et al. 2019

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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Throughout the life cycle of civil assets, construction, operation and maintenance phases require monitoring to assure reasonable decision makings. Current methods always involve speciallyassigned personnel conducting on-site inspections, which are work-intensive, time-consuming and errorprone. Computer vision, as a powerful alternative to manual inspection, has been extensively studied during the past decades. On the basis of existing summary papers, this paper reviews a wide range of literatures, including journal articles, conference proceedings and other resources. Current applications of computer vision during construction, operation and maintenance stages of civil structures are concluded, with a special focus on operation and maintenance phase. This review aims to provide a comprehensive insight about the utilization of computer vision in civil engineering and an inspiring guidance for future research.

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Visual analysis of asphalt pavement for detection and localization of potholes

Cited by 75 publications

References 12 publications

Detection and Segmentation of Cement Concrete Pavement Pothole Based on Image Processing Technology

Detection and Segmentation of Cement Concrete Pavement Pothole Based on Image Processing Technology

Research on Relationships among Different Distress Types of Asphalt Pavements with Semi‐Rigid Bases in China Using Association Rule Mining: A Statistical Point of View

Computer Vision Techniques in Construction, Operation and Maintenance Phases of Civil Assets: A Critical Review

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