Pothole Detection and Cost Estimation

This paper presented an intelligent Pothole Detection and Cost Estimation System using YOLOv8, Deep Learning, Computer Vision, and OpenCV technologies. The proposed system successfully automates the process of detecting potholes from road images and video streams, reducing the dependency on traditional manual inspection methods. By utilizing advanced object detection techniques, the system accurately identifies potholes and determines their location in real time. Furthermore, image processing and dimension estimation techniques enable the calculation of pothole length, width, area, and depth, providing valuable information for road condition assessment. The estimated dimensions are further used to calculate repair material requirements and maintenance costs, assisting transportation authorities in effective budgeting and resource allocation. The generated reports support informed decision-making and improve road maintenance planning. Experimental results demonstrate that the proposed approach is reliable, efficient, and scalable for real-world applications. Overall, the system contributes to enhanced road safety, reduced maintenance efforts, and improved infrastructure management. Future enhancements may include GPS-based pothole mapping, drone-assisted road inspection, cloud integration, and predictive maintenance analytics for smart transportation systems.

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