Deep Learning Framework for Automated Pothole Detection

Pothole detection plays a crucial role in road surface inspection and repair. Traditional methods rely on visual inspection by experienced personnel or expensive machinery, making the process time-consuming and costly. This abstract focuses on the use of deep learning models to analyze road images and accurately identify road deterioration, specifically targeting pothole detection. The research aims to train and test various deep learning techniques to determine the most effective model with high accuracy for pothole detection. By leveraging deep learning algorithms, this research aims to overcome the limitations of manual inspection and expensive machinery in pothole detection. The proposed approach involves training deep learning models on road images to recognize and classify potholes accurately. By evaluating and comparing multiple deeplearning techniques. Implementing an automated pothole detection system based on deep learning techniques can significantly enhance the efficiency and cost-effectiveness of road surface inspection and repair. The findings of this research will contribute to the development of advanced methods for early detection and prompt repair of potholes, ultimately improving road safety and minimizing transportation disruptions caused by road deterioration.