We present LiteYOLOv8, a lightweight pedestrian detection architecture optimized for real-time edge deployment. Built upon YOLOv8n, it integrates a FasterNet backbone for efficient feature extraction, a BiFPN for multi-scale fusion, and SimAM attention to refine spatial focus. On the CityPersons dataset, LiteYOLOv8 improves mAP@50 by 2.2%, reduces parameters by 65%, and lowers FLOPs by 21%. Real-device evaluation on a Raspberry Pi 5 confirms low latency and minimal resource usage, demonstrating its suitability for autonomous and surveillance applications.
Tariq et al. (2026) studied this question.