Real-time Potholes Detection and Localization using YOLOv8

The maintenance of road infrastructure involves significant efforts, and the detection of potholes is key to identifying areas in need of repair. In this research, we propose an innovative approach by combining YOLOv8, the eighth version of the You Only Look Once (YOLO) architecture, and the Roboflow platform to enhance accuracy and efficiency in pothole detection. Our approach begins with the collection of a dataset encompassing various road conditions and pothole sizes. We preprocess the data using Roboflow to ensure dataset consistency and quality. YOLO- 8 is then trained using the processed dataset, harnessing the power of deep learning technology to produce a superior object detection model. With YOLOv8, we present a reliable solution for real-time monitoring and maintenance of pothole-ridden roads. The implementation of this technology is expected to support more effective road infrastructure management, enabling swift responses to road conditions requiring repair. This research contributes to the development of an efficient and innovative pothole detection system.