Author ORCID Identifier
https://orcid.org/0000-0002-0480-989X, https://orcid.org/0000-0003-1766-2441, https://orcid.org/0000-0003-2841-9738
Document Type
Conference Paper
Disciplines
Computer Sciences
Abstract
Road maintenance and the early detection of road defects rely on routine pavement inspections. While advanced 3D laser profiling systems have the capability to automatically identify certain types of distress such as cracks and ruts, more complex pavement damage, including patches, often require manual identification. To address this limitation, this study proposes an automated patch detection system that employs object detection techniques. The results demonstrate the ability of object detection models to accurately identify patches in laser profiling images, indicating that the proposed approach has the capability to significantly enhance automation in visual inspection processes. This has the potential for significant cost reduction in inspections, improved safety conditions during checks, and acceleration of the current manual inspection processes.
DOI
https://doi.org/10.1007/978-3-031-44137-0_18
Recommended Citation
Syed, I.H., McKeever, S., Feighan, K., Power, D., O’Sullivan, D. (2023). A Deep Learning-Based Object Detection Framework for Automatic Asphalt Pavement Patch Detection Using Laser Profiling Images. In: Christensen, H.I., Corke, P., Detry, R., Weibel, JB., Vincze, M. (eds) Computer Vision Systems. ICVS 2023. Lecture Notes in Computer Science, vol 14253. Springer, Cham. DOI: 10.1007/978-3-031-44137-0_18
Funder
Science Foundation Ireland
Publication Details
International Conference on Computer Vision Systems (ICVS23), Vienna, Austria
https://doi.org/10.1007/978-3-031-44137-0_18