Shadow Erosion and Nighttime Adaptability for Camera-Based Automated Driving Applications
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912565016133632 |
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| author | Sabry, Mohamed Schroeder, Gregory Varughese, Joshua Olaverri-Monreal, Cristina |
| author_facet | Sabry, Mohamed Schroeder, Gregory Varughese, Joshua Olaverri-Monreal, Cristina |
| contents | Enhancement of images from RGB cameras is of particular interest due to its wide range of ever-increasing applications such as medical imaging, satellite imaging, automated driving, etc. In autonomous driving, various techniques are used to enhance image quality under challenging lighting conditions. These include artificial augmentation to improve visibility in poor nighttime conditions, illumination-invariant imaging to reduce the impact of lighting variations, and shadow mitigation to ensure consistent image clarity in bright daylight. This paper proposes a pipeline for Shadow Erosion and Nighttime Adaptability in images for automated driving applications while preserving color and texture details. The Shadow Erosion and Nighttime Adaptability pipeline is compared to the widely used CLAHE technique and evaluated based on illumination uniformity and visual perception quality metrics. The results also demonstrate a significant improvement over CLAHE, enhancing a YOLO-based drivable area segmentation algorithm. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_08551 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Shadow Erosion and Nighttime Adaptability for Camera-Based Automated Driving Applications Sabry, Mohamed Schroeder, Gregory Varughese, Joshua Olaverri-Monreal, Cristina Computer Vision and Pattern Recognition Enhancement of images from RGB cameras is of particular interest due to its wide range of ever-increasing applications such as medical imaging, satellite imaging, automated driving, etc. In autonomous driving, various techniques are used to enhance image quality under challenging lighting conditions. These include artificial augmentation to improve visibility in poor nighttime conditions, illumination-invariant imaging to reduce the impact of lighting variations, and shadow mitigation to ensure consistent image clarity in bright daylight. This paper proposes a pipeline for Shadow Erosion and Nighttime Adaptability in images for automated driving applications while preserving color and texture details. The Shadow Erosion and Nighttime Adaptability pipeline is compared to the widely used CLAHE technique and evaluated based on illumination uniformity and visual perception quality metrics. The results also demonstrate a significant improvement over CLAHE, enhancing a YOLO-based drivable area segmentation algorithm. |
| title | Shadow Erosion and Nighttime Adaptability for Camera-Based Automated Driving Applications |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.08551 |