Pothole Detection and Management System using AI

Potholes put pedestrian and vehicular safety in peril, creating a traffic hazard. In the majority of developing nations, it ranks among the main factors in traffic accidents that result in the loss of life and property. In order to identify potholes quickly and accurately, this project aims to assess the performance of cutting-edge neural network techniques like YOLO and Faster R-CNN with VGG16 and ResNet-18 architectures. A new YOLOv2 architecture is also proposed to handle the "pothole" and "regular road" class imbalance issue, and its performance is evaluated in comparison to other object identification algorithms using accuracy, recall, intersection over union, and frames processed per second (FPS).