Lane detection using deep learning for rainy conditions

Ghani, Hadhrami Ab and Daud, Atiqullah Mohamed and Besar, Rosli and Sani, Zamani Md and Kamaruddin, Mohd Nazeri and Syahali, Syabeela (2023) Lane detection using deep learning for rainy conditions. In: 9th International Conference on Computer and Communication Engineering, ICCCE 2023, 15 August 2023through 16 August 2023, Kuala Lumpur.

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Abstract

Prior research has shown that various road marker classification mechanisms in clear or dry weather conditions have high accuracy performance. However, the performance tends to be lower under rainy driving conditions due to the reduced quality of the road image when detecting the five classes of road markers which are Single, Single-Single, Dashed, Solid-Dashed, and Dashed-Solid. To address this challenging condition, lane marker detection based on deep learning approach is proposed in this paper. The target weather condition is rainy, which is very challenging as it causes the surface of the roads, especially the area which includes the lane marker to become blurry and unclear due to the rainwater. In order to carefully select the right features of the road such that the lane marker can be classified and detected successfully. The lane marker object is captured from the frames of the video clips taken from established published video datasets. With this fast and better lane marker detection, the achievable classification precision is satisfactory although the weather is rainy.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Convolutional neural net-works, Lane detection, Machine vision
Divisions:	Faculty Of Electrical Technology And Engineering
Depositing User:	Maizatul Najwa Ahmad
Date Deposited:	10 Oct 2024 10:18
Last Modified:	10 Oct 2024 10:18
URI:	http://eprints.utem.edu.my/id/eprint/27944
Statistic Details:	View Download Statistic

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