Citation

Kamaruddin, Mohd Nazeri and Martin, Aerun and Md Sani, Zamani and Ab. Ghani, Hadhrami (2023) Road Marker Classification Mechanism Using Deep Learning Analysis in Foggy Condition. In: 2nd FET PG Engineering Colloquium Proceedings 2023, 1-31 December 2023, Multimedia University, Malaysia. (Submitted)

Text (Poster) 13. PPM Poster - Aerun Martin - Muharniza Azinita Binti Musa.pdf - Submitted Version Restricted to Registered users only Download (1MB)

Abstract

This study introduces an innovative ResNet-based dehazing algorithm tailored for road images, crucial for the performance of Advanced Driver Assistance Systems (ADAS) in challenging atmospheric conditions like fog, haze, and smog. The algorithm excels in enhancing image visibility in hazy environments, outperforming existing methods in terms of Peak Signal-to-Noise Ratio (PSNR). By significantly improving visibility, this ResNet-based approach directly enhances the accuracy of classifying road markers, particularly single dashed and double solid markers, essential for ADAS functionality.

Item Type:	Conference or Workshop Item (Poster)
Uncontrolled Keywords:	Deep Learning
Subjects:	Q Science > QA Mathematics
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	03 Apr 2024 01:01
Last Modified:	03 Apr 2024 02:11
URII:	http://shdl.mmu.edu.my/id/eprint/12338

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