Published Paper Details:

The project "Pothole Detection in a Video" aims to address critical issue of the road maintenance and safety by leveraging computer vision techniques. Potholes pose a significant threat to both drivers and pedestrians, leading to accidents, vehicle damage, and increased road maintenance costs. The proposed system utilizes algorithms to detect automatically and locate potholes within video footage, providing a proactive solution for timely repairs and improved road safety. The alert will be given based on type of the vehicle, it ensures that whether the potholes damages the vehicle or not and then alerts the vehicle based on that. The project employs computer vision methods to analyze video frames, identifying the key features that are associated with potholes, such as shape, depth, and texture. Through systematic frame scanning, potential potholes are detected and their locations marked. To enhance accuracy, the system incorporates machine learning, allowing the algorithm to learn from a dataset of labeled pothole images, minimizing false positives and adapting to various road conditions. The significance of this project lies in their potential to contribute to smart city initiatives and transportation management systems, aiding in efficient maintenance of the road infrastructure. By automating the pothole detection process, the system facilitates quick response times for repairs, ultimately improving the overall quality and safety of road networks. This project provides an opportunity for to explore the intersection of computer vision, machine learning, and real-world applications, making a tangible impact on urban infrastructure challenges.