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arxiv: 2605.16397 · v1 · pith:UPYB3WCYnew · submitted 2026-05-12 · 💻 cs.CV · cs.AI

Trajectory-Aware Adaptive Inference in Object Detection Models

Grigorios Papanikolaou , Ioannis Kontopoulos , Giannis Spiliopoulos , Dimitris Zissis , Konstantinos Tserpes This is my paper

Pith reviewed 2026-05-20 22:29 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords object detectionearly exitadaptive inferencetrajectory datamaritime navigationYOLOv8efficiencyreal-time perception
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The pith

Trajectory data from vessels triggers early exits in object detectors to reduce compute while holding detection quality steady.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that GPS trajectory cues can guide adaptive computation in object detection for maritime navigation. It adds an early-exit option to a YOLOv8 model so that frames with distant or slowly approaching vessels use only part of the network. Frames with close, fast-converging vessels still run the full detector. This produces lower inference time and cost than always using the complete model, yet keeps detection performance at an acceptable level. Readers would care because autonomous vessels need fast, low-power perception to operate safely in real time.

Core claim

The method evaluates scene complexity per frame or per second by measuring inter-vessel distance and the rate at which that distance shrinks. When these values indicate low difficulty, the detector activates only a subset of its layers via an early-exit path; otherwise the full model runs. Experiments show this trajectory-aware choice keeps detection results satisfactory while cutting inference time and computational cost relative to non-adaptive full-model inference.

What carries the argument

Early-exit mechanism driven by inter-object distance and convergence rate as proxies for frame difficulty, deciding full versus partial network activation inside a YOLOv8 detector.

If this is right

  • Real-time perception on vessels becomes feasible on hardware with limited compute budget.
  • The system can allocate full computation only to the most urgent close-range encounters.
  • Overall energy use drops in continuous monitoring scenarios without requiring hardware changes.
  • A continuous dial between accuracy and speed appears by adjusting the distance and speed thresholds.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same distance-based proxy might be tested in road-vehicle detection where relative speed data is also available from radar or V2X.
  • Combining the exit decision with other cheap cues such as image brightness or motion blur could make the trigger more robust.
  • Long-term deployment would need checks on how well the proxy works when visibility drops due to fog or night conditions.

Load-bearing premise

Inter-object distance and its rate of decrease are sufficient and reliable indicators of whether skipping later network layers will still produce good detections.

What would settle it

A set of test frames in which vessels close at high speed but the early-exit path misses objects that the full model correctly detects would show the assumption does not hold.

Figures

Figures reproduced from arXiv: 2605.16397 by Dimitris Zissis, Giannis Spiliopoulos, Grigorios Papanikolaou, Ioannis Kontopoulos, Konstantinos Tserpes.

Figure 1
Figure 1. Figure 1: This figure presents the changes between the usage of heads based on the signal derived from trajectories. [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
read the original abstract

The increasing integration of sensors in autonomous maritime navigation has led to large-scale multimodal datasets, raising challenges in achieving efficient real-time perception. In such systems, object detection and trajectory perception of nearby vessels are tightly coupled, particularly in dynamic environments such as maritime navigation. However, the efficiency of object detection models during inference remains an often-overlooked aspect. To this end, we build upon an existing object detection framework by incorporating GPS trajectory data into the inference process to enable input-adaptive computation. Specifically, we introduce an early-exit mechanism in a YOLOv8-based detector that incorporates motion cues - such as inter-vessel distances. Frames of vessels that are separated by short distances, converging with high speed, are processed using the full model, while only a subset of the network's architecture is activated otherwise. The difficulty degree (or scene complexity) of a frame or set of frames per second is evaluated by leveraging inter-object distance and the rate at which the distance between them decreases. Experimental results demonstrate that this strategy maintains satisfactory detection performance while significantly reducing inference time and computational cost, thus enabling a flexible trade-off between accuracy and efficiency compared to full-model inference.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes a trajectory-aware adaptive inference method for object detection in maritime navigation. It augments a YOLOv8 detector with an early-exit mechanism that uses GPS-derived inter-vessel distances and closing rates as proxies for scene complexity: frames with large separations or low convergence speeds activate only a subset of the network, while close or rapidly approaching vessels trigger full-model inference. The central claim is that this maintains satisfactory detection performance while substantially lowering inference time and compute cost relative to always-on full inference.

Significance. If the proxy-based early-exit decisions prove reliable, the approach could enable practical efficiency gains in real-time multimodal perception systems for autonomous vessels by exploiting readily available GPS data. It extends standard early-exit techniques with domain-specific motion cues rather than purely image-based difficulty estimators, offering a concrete accuracy-efficiency trade-off. The work is most relevant to resource-constrained maritime settings where external trajectory information is already present.

major comments (2)
  1. [§3] §3 (Method): The central assumption that inter-object distance and closing rate are sufficient proxies for detection difficulty is load-bearing for the accuracy claim, yet no correlation analysis, ablation against alternative complexity signals, or per-regime breakdown of false-negative/localization error rates is provided to show that 'easy' frames (large distances) retain detection quality close to full-model inference.
  2. [§4] §4 (Experiments): The abstract asserts that experiments demonstrate maintained detection performance with reduced cost, but the manuscript supplies no quantitative metrics (mAP, latency reductions), baselines (vanilla YOLOv8, other adaptive methods), dataset details, or ablation tables, leaving the empirical support for the accuracy-efficiency trade-off unverified.
minor comments (2)
  1. [Abstract] Abstract: Key quantitative results (e.g., percentage reduction in FLOPs or inference time, mAP delta) should be included to allow readers to assess the claimed trade-off without reading the full text.
  2. [§3.1] Notation: The precise definition of the distance and closing-rate thresholds used to label frames 'easy' versus 'hard' should be stated explicitly, including whether they are fixed or learned.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. The feedback identifies key areas where additional analysis and quantitative detail will strengthen the presentation of our trajectory-aware early-exit approach. We respond to each major comment below and commit to the corresponding revisions.

read point-by-point responses

  1. Referee: [§3] §3 (Method): The central assumption that inter-object distance and closing rate are sufficient proxies for detection difficulty is load-bearing for the accuracy claim, yet no correlation analysis, ablation against alternative complexity signals, or per-regime breakdown of false-negative/localization error rates is provided to show that 'easy' frames (large distances) retain detection quality close to full-model inference.

    Authors: We agree that explicit validation of the proxy assumption is necessary. In the revised manuscript we will add a dedicated subsection to §3 containing (i) correlation analysis between inter-vessel distance/closing speed and detection difficulty metrics (e.g., mAP drop under early exit), (ii) ablations comparing our motion cues against alternative signals such as image entropy and model uncertainty, and (iii) per-regime tables breaking down false-negative rates and localization error for distance and speed thresholds. These additions will directly demonstrate that performance on 'easy' frames remains close to full-model inference. revision: yes

  2. Referee: [§4] §4 (Experiments): The abstract asserts that experiments demonstrate maintained detection performance with reduced cost, but the manuscript supplies no quantitative metrics (mAP, latency reductions), baselines (vanilla YOLOv8, other adaptive methods), dataset details, or ablation tables, leaving the empirical support for the accuracy-efficiency trade-off unverified.

    Authors: We acknowledge that the current experimental section lacks the requested quantitative detail. We will expand §4 to report concrete mAP values, per-frame latency reductions, direct comparisons against vanilla YOLOv8 and other adaptive baselines, full dataset specifications (size, splits, maritime conditions), and ablation tables for varying distance and closing-speed thresholds. These revisions will make the accuracy-efficiency trade-off verifiable. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation uses independent external trajectory data

full rationale

The paper defines an early-exit policy for a YOLOv8 detector by directly mapping inter-vessel distance and convergence rate (computed from external GPS trajectories) to a binary decision between full-model and partial-network inference. This mapping is presented as an input-driven heuristic for scene complexity without any fitted parameters, self-referential definitions, or load-bearing self-citations that would reduce the claimed accuracy-efficiency trade-off back to the method's own outputs. The central logic remains an engineering rule applied to standard detection architecture and is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 0 axioms · 0 invented entities

Based solely on the abstract, the approach implicitly depends on tuned thresholds for distance and closing rate that are not specified; no explicit free parameters, axioms, or new entities are named.

free parameters (1)
  • distance and closing-rate thresholds
    Used to classify frames as simple or complex and decide early exit; values must be chosen or fitted but are not reported.

pith-pipeline@v0.9.0 · 5749 in / 1087 out tokens · 30487 ms · 2026-05-20T22:29:16.463607+00:00 · methodology

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Reference graph

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