Towards Safer Roads: Intelligent Pothole Detection with YOLOv11

The preservation of road infrastructure and the safety of vehicular traffic are critical challenges in modern transportation systems. Potholes, in particular, pose significant risks to road users and contribute to accidents, making their timely detection and repair essential. This study proposes the concept design and realization of an intelligent automated pothole detection system utilizing YOLOv11, A cutting-edge object detection model powered by deep learning algorithms. In this approach, deep learning algorithms are trained to detect potholes in image data collected from road surfaces under varying environmental conditions, including different lighting and weather scenarios. The real-time capabilities of YOLOv11 enable accurate and rapid detection, making the system suitable for integration into autonomous vehicles, traffic monitoring systems, and road maintenance operations. The system is developed using Python, OpenCV, and deep learning frameworks such as TensorFlow and PyTorch. Its performance is evaluated against Important measurements like processing speed, intersection over union (IoU), and mean average precision (mAP) are used in order to measure both accuracy as well as computing speed. The framework for detecting potholes, which was created, can help facilitate the improvement of intelligent transportation systems through the incorporation of automation, resulting in enhanced road safety and simpler maintenance procedures.