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Title	Deep Vision: Lane Detection In Its: A Deep Learning Segmentation Perspective
ID_Doc	18091
Authors	Santhiya P.; Jebadurai I.J.; Paulraj G.J.L.; Jenefa A.; Karan S.K.; Naveen.v E.
Year	2024
Published	Proceedings - 2024 2nd International Conference on Inventive Computing and Informatics, ICICI 2024
DOI	http://dx.doi.org/10.1109/ICICI62254.2024.00012
Abstract	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. © 2024 IEEE.
Author Keywords	Autonomous Vehicles; Computer Vision; Deep Learning; Image Segmentation; Lane Detection; Urban Traffic Management

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