Potholes are structural flaws in the road's surface that can impair the road's ability to carry vehicles and result in serious accidents. In this article, we suggest a pothole detection system that is effective and uses deep learning algorithms to identify potholes on the road automatically. With the preprocessed dataset, four models—YOLO V3, SSD, HOG with SVM, and Faster R-CNN—are trained and evaluated. Initial photos with and without potholes are gathered and annotated in step one. With the processed picture dataset, the four models are trained and evaluated for accuracy and loss comparison in phase two. The performance and accuracy of all four models are then examined. According to the trial findings, the YOLO V3 model excels due to its quicker and more accurate detection outcomes.
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