Visual analysis of asphalt pavement for detection and localization of potholes. (October 2018)

• Record Type: Journal Article
• Authors: Yousaf, Muhammad Haroon; Azhar, Kanza; Murtaza, Fiza; Hussain, Fawad
• Is Part Of: Advanced engineering informatics. Volume 38(2018)
• Page Start: 527
• Page End: 537
• Publication Date: 2018-10
• Subjects: Pavement distress -- Potholes -- Computer vision -- Bag of words; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2018.09.002
• Languages: English
• ISSNs: 1474-0346

Abstract

Abstract: Identifying and restoring distresses in asphalt pavement have key significance in durability and long life of roads and highways. A vast number of accidents occurs on the roads and highways due to the pavement distresses. This paper aims to detect and localize one of the critical roadway distresses, the potholes, using computer vision. We have processed images of asphalt pavement for experimentation containing the pothole and non-pothole regions. We proposed a top-down scheme for the detection and localization of potholes in the pavement images. First, we classified pothole/non-pothole images using a bag of words (BoW) approach. We employed and computed famous scale-invariant feature transform (SIFT) features to establish the visual vocabulary of words to represent pavement surface. Support vector machine (SVM) is employed for the training and testing of histograms of words of pavement images. Secondly, we proposed graph cut segmentation scheme to localize the potholes in the labelled pothole images. This paper presents both, subjective and objective evaluation of potholes localization results with the ground truth. We evaluated the proposed scheme on a pavement surface dataset containing the wide-ranging pavement images in different scenarios. Experimentation results show that we achieved an accuracy of 95.7% for the identification of pothole images with significant precision and recall. Subjective evaluation of potholes localization results in high recall with relatively good accuracy. However, the objective assessment shows the 91.4% accuracy for localization of potholes.