Pothole Detection and Smart Navigation System
DOI:
https://doi.org/10.15662/IJEETR.2026.0802404Keywords:
Pothole Detection, YOLO, Deep Learning, Cloud Computing, GIS, Route OptimizationAbstract
Potholes are a major issue in road infrastructure, leading to accidents, vehicle damage, and inefficient transportation. Traditional pothole detection methods are manual and inefficient. This paper proposes a smart, user-driven pothole detection system that integrates deep learning, cloud computing, and geographic mapping. Users capture pothole images using mobile devices, which are uploaded to the cloud and processed using a YOLO-based object detection model. The detected potholes are mapped using GPS coordinates and displayed on an interactive map. Additionally, the system suggests alternate routes to users based on pothole density using path optimization algorithms. The data collected can also be shared with government authorities for maintenance purposes. Experimental results show improved accuracy and real-time performance, making the system scalable and efficient.
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