Temporal Preservation over Processing: Diagnosing and Designing Spatiotemporal Single-Stage Video Detectors

2026-06-30

AI Summary

This work addresses the ambiguity regarding whether existing single-stage video object detectors genuinely leverage temporal context, as standard evaluation metrics often fail to reveal their actual reliance on temporal information. To this end, we propose TemporalLens, a diagnostic framework that quantifies a model’s temporal dependency through controlled perturbations—including temporal shuffling, structured occlusion, and redundancy injection. Furthermore, we design YOLO-3D based on YOLOv8, explicitly preserving the temporal dimension within the backbone to enhance genuine temporal reasoning. Experiments demonstrate that TemporalLens effectively distinguishes between stacked 2D models and true temporal architectures, while YOLO-3D achieves an average mAP@50 improvement of 3.7 percentage points with 32-frame inputs, underscoring the critical role of temporal depth in performance gains.

Abstract

Single-stage video object detectors are increasingly deployed in time-critical applications, yet it remains unclear whether these models genuinely reason over temporal context or merely exploit a single informative frame-a gap hidden by standard metrics, which reward correct predictions regardless of how they are reached. We address this from two complementary directions: first, we propose TemporalLens, a model-agnostic diagnostic framework probing temporal dependence through controlled perturbations, structured occlusions, temporal shuffling, redundancy injection, and resolution degradation, revealing whether a detector actually uses information across time. Applied to stacked-frame 2D detectors and our YOLO-3D architecture, it exposes behavioural differences invisible to mAP: stacked 2D models collapse when the target frame is removed, while spatiotemporal models recover predictions from earlier frames, a signature of real temporal reliance. Second, we detail YOLO-3D, a modular real-time spatiotemporal detector built on YOLOv8, and show that simply preserving temporal depth through the backbone is the dominant performance driver (+3.7 pp mAP@50 at 32 frames averaged across scales). Together, the diagnostics and architecture turn "does this detector reason over time?" into a measurable, actionable question.

Problem

• temporal reasoning
• video object detection
• single-stage detectors
• temporal context
• model diagnostics

Innovation

• TemporalLens
• spatiotemporal detection
• single-stage video object detection
• temporal reasoning
• YOLO-3D