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Vehicle-Mounted Real-Time Road Damage Detection System Using YOLOv8 and Edge Computing
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Author(s): L. Churchill (M. Kumarasamy College of Engineering, India), A. Selvi (M. Kumarasamy College of Engineering, India), S. Akshaykumar (M. Kumarasamy College of Engineering, India), G. Kaviyarasan (M. Kumarasamy College of Engineering, India)and A. S. Shivapriyan (M. Kumarasamy College of Engineering, India)
Copyright: 2027
Pages: 34
Source title:
GIS Applications for Enhancing Sustainable Operational Performance of Manufacturing Industries
Source Author(s)/Editor(s): Kamalakanta Muduli (Papua New Guinea University of Technology, Papua New Guinea), Sujoy Kumar Jana (Papua New Guinea University of Technology, Papua New Guinea), Rashmi Prava Das (CV Raman Global University, Odisha, India)and Indrajit Pal (Asian Institute of Technology, Thailand)
DOI: 10.4018/979-8-3373-7941-8.ch010
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Abstract
This paper presents a real-time road health monitoring system that overcomes the limitations of manual road inspections. The system uses cameras and GPS modules mounted on public vehicles to capture live road images, which are processed on edge devices using a YOLOv8-based deep learning model. It automatically detects potholes, cracks, and surface irregularities, assigns severity scores, and tags each defect with precise GPS coordinates. The data is sent to a centralized PostgreSQL database with PostGIS for spatial mapping. A React-based administrative dashboard helps authorities assess road conditions and prioritize repairs, while a public React web portal allows citizens to report issues. Automated and public reports are cross-verified to avoid duplication, making the system cost-effective, scalable, and reliable.
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