Energy-efficient real-time vehicle detection using hybrid conditional frame skipping with YOLOv8
ICT EXPRESS, cilt.12, sa.4, ss.1035-1040, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 12 Sayı: 4
- Basım Tarihi: 2026
- Doi Numarası: 10.1016/j.icte.2026.07.004
- Dergi Adı: ICT EXPRESS
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Sayfa Sayıları: ss.1035-1040
- Anahtar Kelimeler: Video object detection, Energy-efficient deep learning, Frame skipping, Conditional inference, Real-time video analytics
- Atatürk Üniversitesi Adresli: Evet
Özet
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.