Vision-Based Robust Lane Detection and Tracking System

Lane detection and tracking are crucial for modern vehicle navigation systems, especially for ADAS and autonomous vehicles. Traditional methods often fail under adverse conditions such as poor lighting, bad weather, and inconsistent road markings. This paper presents a novel approach using YOLOv5, an advanced object detection model known for its real-time performance and accuracy, to detect lane boundaries directly from images. We improved its robustness in challenging scenarios by adapting YOLOv5 for lane detection and introducing innovative post-processing techniques. These techniques include refining lane predictions, handling occlusions, and reducing noise. Extensive experiments on datasets from various conditions (daytime, nighttime, and adverse weather) show that our method outperforms existing approaches. The proposed YOLOv5-based system offers a promising solution for real-world driving challenges, enhancing the precision and dependability of lane recognition and tracking and positively impacting road safety and autonomous vehicle technologies.