Enhancing road image clarity with residual neural network dehazing mode

Kamaruddin, Mohd Nazeri and Martin, Aerun and Md. Sani, Zamani and Abdul Ghani, Hadhrami (2024) Enhancing road image clarity with residual neural network dehazing mode. IAES International Journal of Artificial Intelligence (IJ-AI), 13 (4). pp. 4147-4157. ISSN 2089-4872

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Abstract

Lane markers, or road markers, are the painted lines on a roadway that separate different lanes of traffic. Lane markers guide drivers and ensure orderly vehicle flow. They are essential for advanced driver assistance systems (ADAS), providing reference points for vehicle positioning on the road. These markers enable ADAS to give warnings, assistance, and automation features that enhance driver safety and convenience. However, unpredictable illumination, such as a foggy environment, can suppress marker visibility, impacting ADAS's performance. Deep learning-based methods are well-known for their superiority in handling various haze patterns. This paper presents a residual network (ResNet)-based deep learning model to improve road image clarity impacted by fog. The residual neural network dehaze model (RNN-D) utilises a joint loss function to produce haze-free images with improved lighting conditions and enhanced details. The model was trained, validated, and fine-tuned using hazy and corresponding non-hazy datasets to ensure that the model is quantitatively superior in the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM). RNN-D achieved an average PSNR of 27.98 and SSIM of 0.8 on multiple opensourced datasets. The proposed algorithm's superior performance and visually appealing results make it a powerful tool for real-world image dehazing applications.

Item Type:	Article
Uncontrolled Keywords:	Advanced driver assistance system, Deep learning, Dehazing algorithm, Image enhancement, Lane markers
Divisions:	Faculty of Engineering Technology
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	13 Apr 2026 07:25
Last Modified:	13 Apr 2026 07:25
URI:	http://eprints.utem.edu.my/id/eprint/29632
Statistic Details:	View Download Statistic

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