Shadow Detection

Shadow detection in images and videos is a crucial computer vision task aiming to accurately identify and segment shadowed regions, often as a precursor to shadow removal or other image processing. Current research emphasizes improving the accuracy of shadow detection, particularly in challenging scenarios like differentiating shadows from similarly dark objects or handling shadows in complex backgrounds, employing techniques like transformer-based architectures, diffusion models, and adaptations of powerful pre-trained models such as Segment Anything Model (SAM). These advancements are significant for applications ranging from enhancing image quality and enabling realistic video editing to improving the performance of other computer vision systems, such as those used in agriculture and medical imaging. The development of new datasets and evaluation metrics further contributes to the field's progress.