A spatial features and weight adjusted loss infused Tiny YOLO for shadow detection

Abstract

Shadow detection in computer vision is challenging due to the difficulty in distinguishing shadows from similarly colored or dark objects. Variations in lighting, background textures, and object shapes further complicate accurate detection. This work introduces NS-YOLO, a novel Tiny YOLO variant designed for the specific task of shadow detection under varying conditions. The new architecture includes a small-scale feature extraction network improvised by global attention mechanism, multi-scale spatial attention, and a spatial pyramid pooling block, while preserving effective multi-scale contextual information. In addition, a weight-adjusted CIOU loss function is introduced for enhancing localization accuracy. The proposed architecture addresses shadow detection by effectively capturing both fine details and global context, helping distinguish shadows from similar dark regions. The enhanced loss function improves boundary localization, reducing false detections and improving accuracy. The NS-YOLO is trained end-to-end from scratch on the SBU and ISTD datasets. The experiments show that NS-YOLO achieves a detection accuracy (mAP) of 59.2 % while utilizing only 35.6 BFLOPs. In comparison with existing lightweight YOLO variants that is, Tiny YOLO and YOLO Nano models proposed between 2017–2025, NS-YOLO shows a relative mAP improvement of 2.5 - 50.1 %. These results highlight its efficiency and effectiveness and make it particularly suitable for deployment on resource-limited edge devices in real-time scenarios, e.g., video surveillance and advanced driver-assistance systems (ADAS).