본문 바로가기
  • Home

Training-Free Feature-Level Interpolation with Sparse-Backbone Scheduling for YOLO Detection in Low-FPS Video

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2026, 31(3), pp.49~59
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : January 9, 2026
  • Accepted : March 12, 2026
  • Published : March 31, 2026

Min-Ho Kim 1 Kyu-Cheol Cho 1

1인하공업전문대학

Accredited

ABSTRACT

In this paper, we propose a training-free method that mitigates degradation in accuracy and temporal stability while improving system efficiency for YOLO-based detectors in low-frame-rate (Low-FPS) environments. The proposed approach preserves the YOLO backbone and neck, caches feature maps (P3 –P5) right before the Detection Head at Anchor frames, and operates in a Sparse-Backbone manner by performing head-only inference on linearly interpolated features from neighboring Anchors for non-anchor frames. Under a system-level end-to-end(E2E) protocol (5 independent runs, mean ± std), it reduces backbone invocations by about 50% while retaining 98.48% of the baseline mean Average Precision at IoU 0.5 (mAP@50), decreasing processing latency (Lat_proc) by 26.60% and improving processing throughput (FPS_proc) by 36.11%. These results confirm that the proposed method can serve as a practical training-free inference module that preserves detection quality and temporal stability while improving system efficiency in Low-FPS and resource-constrained deployments.

Citation status

* References for papers published after 2024 are currently being built.