Asphalt Pavement Potholes Localization and Segmentation using Deep Retina Net and Conditional Random Fields

Rana Ghazanfar Ali (), Syed M. Adnan, Nudrat Nida, Wakeel Ahmad and Farooq Bilal
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Rana Ghazanfar Ali: Department of Computer Science, University of Engineering & TechnologyTaxila, Pakistan
Syed M. Adnan: Department of Computer Science, University of Engineering & TechnologyTaxila, Pakistan
Nudrat Nida: Air University, Islamabad, Aerospace & Aviation CampusKamra, Pakistan
Wakeel Ahmad: Department of Computer Science, University of Engineering & TechnologyTaxila, Pakistan
Farooq Bilal: Department of Computer Science, University of Engineering & TechnologyTaxila, Pakistan

International Journal of Innovations in Science & Technology, 2022, vol. 3, issue 5, 126-139

Abstract: The main aspect of maintaining the roads and highways' durability and long life is to detect potholes and restore them. A huge number of accidents occur on the roads and highways due to the pothole. It also causes financial loss to vehicle owners by damaging the wheel and flat tire. For the strategies of the road management system and ITS (Intelligent Transportation System) service, it is one of the major tasks to quickly and precisely detect the potholes. To solve this problem, we have proposed a deep learning methodology to automatically detect and segment the pothole region within the asphalt pavement images. The detection of the pothole is a challenging task because of the arbitrary shape and complex structure of the pothole. In our proposed methodology, to accurately detect the pothole region, we used RetinaNet that creates the bounding box around the multiple regions. For the segmentation we used Conditional Random Field that segments the detected pothole regions obtained from RetinaNet. There are three steps in our methodology, image preprocessing, Pothole region localization, and Pothole segmentation. Our proposed methodology results show that potholes in the images were correctly localized with the best accuracy of 93.04%. Conditional Random Fields (CRF) also show good results.

Keywords: RetinaNet; Pothole Segmentation; Conditional Random Fields (CRF); CAD tool; Region proposal (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:abq:ijist1:v:3:y:2022:i:5:p:126-139

DOI: 10.33411/IJIST/2021030510

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