Intelligent Deep Learning Based Pothole Detection and Alerting System

Potholes are a common problem in roads and highways around the world, which can cause severe damage to vehicles and create safety hazards for drivers. In recent years, deep learning algorithms have been increasingly used for automated pothole detection. This research offers a deep learning-based algorithm that can detect potholes early using photos and videos, reducing the likelihood of an accident. This model is basically based Faster Region-based Convolutional Neural Network(F-RCNN) and You Only Look Once Version 3(YOLO V3). It also discuss the challenges in detecting potholes, such as variable lighting conditions and noise in the data, and how these challenges have been addressed in previous research. Finally, we provide a comparative analysis of the performance of different deep learning algorithms for pothole detection based on accuracy. There are various pothole identification models that combine the accelerometer with machine learning techniques, but there are fewer pothole detection models that use simply machine learning techniques to detect potholes. The findings of this study suggest that deep learning algorithms can provide accurate and efficient pothole detection solutions that can help road authorities to maintain and repair roads, reduce vehicle damage, and enhance road safety.