Comparative Analysis of YOLOv7 for Improved Object Detection of Road Markings in Low-Light Conditions: Leveraging CLAHE for Low-Light Image Enhancement
Authors: 
John Paul Quilingking Tomas, Angela Gabrielle Carbonell, Carl Patrick Adrian Aguas, Karelle Mizzy BersabalAuthors Info & Claims
John Paul Quilingking Tomas, Angela Gabrielle Carbonell, Carl Patrick Adrian Aguas, Karelle Mizzy BersabalAuthors Info & ClaimsPages 123 - 128
Abstract
Road markings are crucial for ensuring road safety, especially in challenging conditions such as low-light environments. This study investigates the efficacy of Low-Light Image Enhancement (LLIE) techniques, particularly Contrast Limited Adaptive Histogram Equalization (CLAHE), in enhancing road marking detection accuracy. Leveraging the YOLOv7 algorithm, the researchers trained and evaluated models using original and enhanced datasets. The findings suggest that while CLAHE enhances visibility and improves the overall image quality, models trained on the original dataset achieved slightly higher performance metrics in certain classes. However, further analysis of the results reveals nuanced differences in detection accuracy across different lighting conditions. These findings highlight the complex interplay between image enhancement techniques and object detection algorithms in road marking detection.
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Index Terms
- Comparative Analysis of YOLOv7 for Improved Object Detection of Road Markings in Low-Light Conditions: Leveraging CLAHE for Low-Light Image Enhancement
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April 2024
491 pages
ISBN:9798400717055
DOI:10.1145/3669754
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Published: 30 August 2024
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ICCAI 2024
ICCAI 2024: 2024 10th International Conference on Computing and Artificial Intelligence
April 26 - 29, 2024
Bali Island, Indonesia
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