Published Paper Details:

Road lane-line detection is pivotal for autonomous driving systems and advanced driver-assistance systems (ADAS). However, in complex traffic scenes, challenges like shadows, road blurriness, and sparse markings hinder accurate detection and real-time performance. This project addresses these issues by implementing a lane-line detection algorithm using Python and OpenCV. The algorithm enhances detection by preprocessing images, employing techniques like color thresholding and edge detection, and utilizing Hough transform for lane boundary identification. It provides a comprehensive guide covering image processing, computer vision, and OpenCV, along with strategies to overcome challenges. Moreover, it introduces a multi-stage algorithm incorporating preprocessing, feature extraction, instance segmentation for precise lane delineation, and post-processing for refined results. Through experimentation, this approach demonstrates superior performance, ensuring reliable lane detection across diverse road conditions and environments, thereby contributing to the advancement of autonomous driving technology.