Energy-efficient real-time vehicle detection using hybrid conditional frame skipping with YOLOv8

Video-based object detection systems often incur high computational and energy costs in real-time applications. This study proposes a hybrid frame skipping strategy for energy-efficient vehicle detection using YOLOv8n. The method combines motion-aware conditional inference, periodic detector refresh, and online threshold calibration. Experiments on 40 UA-DETRAC videos show that the proposed approach preserves detection accuracy while reducing computational workload. The best configuration (k=3, p=50) achieves only a negligible F1 reduction (ΔF1=-0.0008), reduces mean GPU energy consumption by 34.3%, and increases throughput from 58.7 FPS to 88.7 FPS. Additional analyses confirm favorable accuracy–efficiency trade-offs.