Deep Vision: Lane Detection in ITS: A Deep Learning Segmentation Perspective

Intelligent Transportation Systems (ITS) are critical for enhancing urban mobility and safety in smart cities. The accuracy of lane detection, a fundamental component of ITS, significantly impacts its effectiveness. Traditional lane detection methods often falter under dynamic urban conditions, such as varying lighting and weather. This study introduces a novel deep learning-based image segmentation framework that leverages state-of-the-art neural networks to improve lane detection accuracy. Proposed approach integrates advancements in convolutional neural networks (CNNs) with image segmentation techniques, resulting in a system capable of precise lane identification under a variety of conditions. Through rigorous evaluation on the TuSimple and Udacity datasets, which include a wide range of urban driving scenarios, proposed models, including Unet and SegNet, demonstrate superior performance with accuracy improvements up to 97.62%, outperforming existing methods like LaneNet. This research paves the way for safer and more efficient ITS by providing a robust solution to the challenges of real-time lane detection in complex and variable environments.