Trajectory-Aware Adaptive Inference in Object Detection Models

2026-05-19 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

A new trajectory-aware adaptive inference framework for object detection models, specifically a YOLOv8-based detector, has been developed to enhance efficiency in real-time perception for autonomous maritime navigation. This framework integrates GPS trajectory data, such as inter-vessel distances and closure rates, into the inference process. It employs an early-exit mechanism where frames depicting short distances or high-speed convergence between vessels are processed by the full YOLOv8 model, while less complex scenarios activate only a subset of the network's detection heads (P3, P4, P5). The methodology includes scale-aware fine-tuning of learning rates for individual detection heads based on object size distribution. Experimental results show this strategy significantly reduces inference time and computational cost, with a median inference latency reduction from 10.097 ms/frame to 6.686 ms/frame, while maintaining satisfactory detection performance.

Key takeaway

For Computer Vision Engineers developing real-time object detection systems for autonomous vehicles, particularly in maritime environments, consider implementing trajectory-aware adaptive inference. By dynamically activating YOLOv8's detection heads based on inter-vessel distance and closure rate, you can achieve significant reductions in inference latency and computational cost without substantial performance degradation. This approach allows for a flexible trade-off between accuracy and efficiency, crucial for resource-constrained edge deployments.

Key insights

Integrating trajectory data into YOLOv8's inference enables adaptive computation, reducing latency while preserving accuracy.

Principles

• Scene difficulty correlates with object proximity and relative motion.
• Early-exit mechanisms can dynamically adjust computational effort.
• Scale-aware learning rates optimize head specialization.

Method

The method fine-tunes YOLOv8 with scale-aware learning rates, analyzes trajectory data for scene difficulty (Haversine distance, closure rate), and then dynamically activates a subset or all detection heads based on these motion cues.

In practice

• Use P3 head for low-complexity scenarios to maximize speedup.
• Calibrate learning rates for detection heads based on target dataset object sizes.
• Employ trajectory data to guide dynamic model inference.

Topics

• Trajectory-Aware Inference
• YOLOv8 Detector
• Autonomous Maritime Navigation
• Early-Exit Mechanisms
• Multi-Modal Perception

Best for: Computer Vision Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.