Potholes pose a significant risk to road safety, as they can cause accidents and damage vehicles. This article presents an effective pothole detection system that utilizes deep learning algorithms to automatically identify potholes on the road. Four models, namely YOLO V3, SSD, HOG with SVM, and Faster R-CNN, are trained and evaluated using a preprocessed dataset. In the first step, images with and without potholes are collected and annotated. The second step involves training and evaluating the four models on the processed image dataset, comparing their accuracy and loss. The performance and accuracy of all models are analyzed, and based on the trial results, the YOLO V3 model demonstrates superior detection outcomes in terms of speed and accuracy.
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