Real-Time Infrared Pedestrian Recognition Model on Edge Devices

Abstract Aiming at the lightweight and real-time detection requirements of infrared pedestrians on edge devices, this paper proposes an improved infrared pedestrian recognition model based on YOLOv5n. The model effectively reduces the number of parameters by introducing the GhostConv and SlimNeck modules in the lightweight design. To enhance real-time performance, the model reduces the number of parameters and improves operation speed through the PAGCP pruning strategy. After experimental analysis, a balance between operation speed and accuracy is achieved. The results show that compared to the original model, the proposed model decreases the number of parameters by 1.52M, increases running speed by 21%, and decreases mAP.50 by only 0.4%. The running speed on Jetson Nano reaches 0.12s per image. The proposed model effectively ensures high detection accuracy under lightweight and real-time requirements, providing technical support for the deployment of edge devices in scenarios such as autonomous driving.