Citation

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

Text Enhancing road image clarity with residual neural network dehazing model _ Martin _ IAES International Journal of Artificial Intelligence (IJ-AI).pdf - Published Version Restricted to Repository staff only Download (8MB)

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 of27.98 and SSIM of 0.8 on multiple open-sourced 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:	Deep learning, Dehazing algorithm
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	03 Dec 2024 03:21
Last Modified:	03 Dec 2024 03:21
URII:	http://shdl.mmu.edu.my/id/eprint/13182

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