Shadow Erosion and Nighttime Adaptability for Camera-Based Automated Driving Applications
This work addresses image quality issues for automated driving systems, but it appears incremental as it builds on existing techniques like CLAHE.
The paper tackled the problem of enhancing RGB camera images for automated driving under challenging lighting conditions, proposing a pipeline for shadow erosion and nighttime adaptability that showed significant improvement over the CLAHE technique in illumination uniformity and visual perception metrics, and enhanced a YOLO-based drivable area segmentation algorithm.
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.