Shadow Detection Datasets

Shadow detection datasets are crucial for training computer vision models to identify and delineate shadowed regions in images, a task vital for numerous applications like autonomous driving and robotics. Recent research focuses on improving accuracy, particularly in challenging scenarios like differentiating shadows from similarly colored objects and handling low-light conditions, often employing transformer-based architectures and iterative label refinement techniques to address noisy or incomplete labels in existing datasets. These advancements are driving progress in robust shadow detection, leading to more reliable performance in real-world applications and informing the development of improved algorithms for related tasks such as shadow removal.