FusionU10: enhancing pedestrian detection in low-light complex tourist scenes through multimodal fusion.

With the rapid development of tourism, the concentration of visitor flows poses significant challenges for public safety management, especially in low-light and highly occluded environments, where existing pedestrian detection technologies often struggle to achieve satisfactory accuracy. Although infrared images perform well under low-light conditions, they lack color and detail, making them susceptible to background noise interference, particularly in complex outdoor environments where the similarity between heat sources and pedestrian features further reduces detection accuracy. To address these issues, this paper proposes the FusionU10 model, which combines information from both infrared and visible light images. The model first incorporates an Attention Gate mechanism (AGUNet) into an improved UNet architecture to focus on key features and generate pseudo-color images, followed by pedestrian detection using YOLOv10. During the prediction phase, the model optimizes the loss function with Complete Intersection over Union (CIoU), objectness loss (obj loss), and classification loss (cls loss), thereby enhancing the performance of the detection network and improving the quality and feature extraction capabilities of the pseudo-color images through a feedback mechanism. Experimental results demonstrate that FusionU10 significantly improves detection accuracy and robustness in complex scenes on the FLIR, M3FD, and LLVIP datasets, showing great potential for application in challenging environments.