Nighttime Datasets

Nighttime datasets are crucial for developing computer vision systems capable of robust performance in low-light conditions, a critical need for autonomous driving and other applications. Current research focuses on creating and utilizing these datasets to train models for tasks like semantic segmentation, depth estimation, and object tracking, employing techniques such as domain adaptation, self-supervised learning, and generative adversarial networks to bridge the performance gap between daytime and nighttime scenarios. These efforts are significantly advancing the capabilities of computer vision algorithms in challenging environments, improving the safety and reliability of autonomous systems and other applications reliant on accurate nighttime scene understanding.