Road Lane Detection and Obstacle Recognition using YOLO and OpenCV

Strong Lane detection and object identification are essential for safe navigation in advanced driver assistance systems (ADAS) and autonomous driving. Using OpenCV for lane marker extraction and YOLO (You Only Look Once) for real-time object identification, this paper aims to create an effective lane detecting system. In order to precisely identify lane borders, OpenCV is used to process video frames using methods including Canny edge detection, Hough Transform, and perspective transformation. A thorough road scene analysis is ensured by YOLO, a deep learning-based object detection framework that recognizes cars, pedestrians, and other road impediments in real time. By combining these technologies, lane departure warnings and obstacle avoidance alerts are provided, improving driving safety. This system is appropriate for autonomous vehicles, driver assistance apps, and smart transportation systems since it is built for real-time performance. The suggested method advances intelligent transportation systems by achieving high accuracy and efficiency.