A Comprehensive System for Automated Pothole Detection and Vehicle Speed Management using CNN Technology

Roadside potholes raise maintenance costs for road officials while causing serious harm to vehicle and endangering the safety of drivers and passengers. In this study, we use Convolutional Neural Network (CNN) technology to suggest a complete system for automatic pothole recognition and vehicle speed management. A pothole detection module and a vehicle speed control module are the two major parts of our system. A CNN-based algorithm is used by the pothole recognition module to evaluate the images. The model is trained to recognize potholes and identify them apart from other roadside characteristics. The vehicle speed control mechanism receives an information when a pothole is found. On a dataset of actual road images, the proposed model is tested and the potholes are identified with an accuracy of 99.56%. The proposed system provides a useful and effective solution for pothole recognition and vehicle speed control, which can help reduce accidents, save money on maintenance, and enhance the driving experience in general.