Energy-efficient real-time vehicle detection using hybrid conditional frame skipping with YOLOv8
ICT EXPRESS, vol.12, no.4, pp.1035-1040, 2026 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 12 Issue: 4
- Publication Date: 2026
- Doi Number: 10.1016/j.icte.2026.07.004
- Journal Name: ICT EXPRESS
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Page Numbers: pp.1035-1040
- Keywords: Video object detection, Energy-efficient deep learning, Frame skipping, Conditional inference, Real-time video analytics
- Ataturk University Affiliated: Yes
Abstract
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.