Multiscale RGB-Thermal Fusion for Vulnerable Road User Detection with ScaleFuse

This study focuses on improving the safety of Vulnerable Road Users (VRUs) in traffic by leveraging multispectral imaging and deep learning. We introduce ScaleFuse, a novel RGB-thermal fusion architecture specifically designed to address the limitations of existing multispectral detection methods. Unlike conventional approaches, ScaleFuse performs multiscale feature fusion at intermediate layers of the backbone, adaptively learning spatial and channel-wise importance from both modalities. To ensure robust fusion, we employ the SuperGlue network for precise image alignment, mitigating the common issue of misregistration between RGB and thermal inputs. ScaleFuse is implemented within the YOLOv10 framework, enabling efficient and accurate detection in challenging conditions such as low-light environments, adverse weather, and occlusions. Experimental evaluations on LLVIP, FLIR, and our newly introduced VeNITMSD dataset demonstrate that ScaleFuse consistently outperforms single-modality baselines and previous fusion strategies in terms of accuracy, precision, and recall. The proposed system achieves real-time inference while remaining fully compatible with deployment frameworks such as TensorRT, making it suitable for intelligent transportation applications.