Infrared pedestrian detection using improved UNet and YOLO through sharing visible light domain information

The detection of pedestrians using infrared images is essential for security during automatic driving and monitoring under low illumination or occlusion conditions. However, detecting pedestrians in infrared images can be challenging due to the lack of clear visual cues and background noise. To address these challenges, we proposed an efficient approach based on an improved UNet and YOLO network that shares visible light information from multiple related datasets. The proposed approach achieves high detection accuracies, with mean average precision values of 87.7%, 88%, and 97.5% on three different datasets, FLIR, M3FD, and LLVIP, respectively. Moreover, the designed infrared pedestrian detection network is deployed on an edge computing device and tested with a dual-light handheld camera, achieving a real-time detection speed of 25.6 FPS with a superior trade-off between speed and accuracy. These results demonstrate the effectiveness of the proposed approach in detecting pedestrians in challenging infrared image conditions.