 A deep learning based fast lane detection approachErkan Oğuz, Ayhan Küçükmanisa, Ramazan Duvar and Oğuzhan Urhan Chaos, Solitons & Fractals, 2022, vol. 155, issue C Abstract: Autonomous vehicles have recently been very popular and it seems to be causing a major transformation in the automotive industry. A vital component for autonomous vehicles is lane keeping systems. The performance of lane keeping systems is directly related to the lane detection accuracy. For lane detection, various sensors are commonly used. In this paper, a vision based robust lane detection system using a novel 1-dimensional deep learning approach is proposed. Challenging situations as rain, shadow, and illumination reduces the overall performance of vision based approaches. Experimental results show that the performance of proposed approach outperforms existing approaches in literature including these challenging situations in terms of detection performance versus processing speed assessment. Although deep learning based methods that provide high performance have difficulties on low-capacity embedded platforms, the proposed method stands out as a solution with its significantly lower processing time. Keywords: ADAS; Lane detection; Deep learning; Real-time (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:155:y:2022:i:c:s0960077921010766 DOI: 10.1016/j.chaos.2021.111722 Access Statistics for this article Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros More articles in Chaos, Solitons & Fractals from Elsevier |
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