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arxiv: 2605.08136 · v1 · submitted 2026-05-01 · 💻 cs.CV · cs.AI· cs.RO

Recognition: no theorem link

Benchmarking ResNet Backbones in RT-DETR: Impact of Depth and Regularization under environmental conditions

Pamela Barboza , V\'ictor Castelli , Bel\'en Pereira , Ricardo Grando , Bruna de Vargas , Augusto Calfani
Authors on Pith no claims yet

Pith reviewed 2026-05-12 00:52 UTC · model grok-4.3

classification 💻 cs.CV cs.AIcs.RO
keywords RT-DETRResNet backbonesobject detectionenvironmental variationscompetitive roboticsreal-time detectionbackbone depth
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The pith

The best ResNet backbone for RT-DETR round-object detection depends on whether the environment changes in lighting or background contrast.

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

The authors compare four ResNet backbones inside the RT-DETR detector when spotting round objects under altered lighting or background contrast. They train all models identically and measure how accuracy, confidence scores, and inference time respond to these changes. Accuracy remains near perfect in most tests, but confidence drops under variation while latency stays stable. The key finding is that ResNet50 handles lighting shifts best and ResNet34 handles background shifts best, suggesting intermediate depths give the strongest efficiency-performance balance for robotics applications.

Core claim

Under illumination variation, ResNet50 achieves the best trade-off with near-perfect accuracy, confidence values up to approximately 0.869 and latency around 0.058-0.059 ms. Under background variation, ResNet34 provides the most balanced performance, reaching near-perfect accuracy and higher confidence values up to approximately 0.887. These results indicate that the optimal architecture depends on the type of environmental variation, with intermediate-depth models offering the best balance between performance and efficiency.

What carries the argument

RT-DETR detector equipped with varying ResNet backbones (ResNet-18, -34, -50, -101), evaluated for round object detection under controlled environmental condition changes in lighting and background contrast.

If this is right

  • Classification accuracy approaches or exceeds 1.00 across all tested conditions and backbones.
  • Inference latency remains largely unaffected by environmental variations.
  • Environmental changes affect prediction confidence more than accuracy or speed.
  • ResNet50 excels under illumination changes while ResNet34 excels under background changes.

Where Pith is reading between the lines

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

  • In dynamic robotics settings with unpredictable mixtures of lighting and background shifts, systems might benefit from switching between intermediate-depth backbones based on real-time environment sensing.
  • The consistent high accuracy suggests that backbone depth primarily influences robustness of confidence estimates rather than core detection capability in this setup.
  • Extending these tests to other object types or combined variations could reveal whether the intermediate-depth preference holds more broadly.

Load-bearing premise

That the lighting and background contrast changes used in testing adequately represent real-world competitive robotics environments and produce equivalent model training outcomes across different ResNet depths.

What would settle it

Observing that a deeper ResNet like ResNet101 or shallower like ResNet18 outperforms the intermediates across multiple real robotics datasets with varied lighting and backgrounds would challenge the claim that optimal depth depends on variation type.

Figures

Figures reproduced from arXiv: 2605.08136 by Augusto Calfani, Bel\'en Pereira, Bruna de Vargas, Pamela Barboza, Ricardo Grando, V\'ictor Castelli.

Figure 1
Figure 1. Figure 1: Schematic representation of the experimen [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Sample images from the Urubots Ball De￾tection dataset showing the different object classes and bounding box annotations under varying light￾ing conditions [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Sequential frames from a real-time inference [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Trade-off among average accuracy, average [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: highlights the best result obtained in the background variation experiment, corre￾sponding to the White Background, Full Light, and Dropout 0.2 condition [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
read the original abstract

Visual perception plays a central role in competitive robotics, where environmental variations can directly affect real-time detection performance. The related literature on transformer-based detectors lack information regarding the impact of backbone scale and environmental settings on model performance. This work presents a comparative evaluation of RT-DETR for detecting round objects under environmental and hyperparameter variations relevant to competitive robotics. Four ResNet backbones (ResNet18, ResNet34, ResNet50, and ResNet101) were compared using dropout rates, analyzing their effect on confidence and accuracy. All models were trained under the same configuration and evaluated under changes in lighting and background contrast. Environmental conditions primarily impact prediction confidence, while inference latency remains largely unaffected and classification accuracy stays consistently high, approaching or above 1.00 in most cases. Two distinct behaviors were observed. Under illumination variation, ResNet50 achieves the best trade-off, combining near-perfect accuracy, confidence values up to approximately 0.869 and latency around 0.058-0.059 ms. Under background variation, ResNet34 provides the most balanced performance, reaching near-perfect accuracy and higher confidence values up to approximately 0.887. These results indicate that the optimal architecture depends on the type of environmental variation, with intermediate-depth models offering the best balance between performance and efficiency.

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 / 3 minor

Summary. The manuscript benchmarks four ResNet backbones (ResNet18, ResNet34, ResNet50, ResNet101) within the RT-DETR detector for round-object detection in competitive robotics. All models are trained under an identical configuration and evaluated on controlled changes in illumination and background contrast. The central claims are that environmental variations primarily degrade prediction confidence while leaving accuracy near 1.0 and latency essentially unchanged, that ResNet50 offers the best trade-off under illumination variation (confidence up to ~0.869), and that ResNet34 is optimal under background variation (confidence up to ~0.887), implying that intermediate-depth backbones provide the best performance-efficiency balance depending on the perturbation type.

Significance. If the empirical comparisons hold, the work supplies practical guidance for backbone selection in real-time robotics perception under variable lighting and backgrounds. The controlled protocol and observation that accuracy remains high while confidence is sensitive are useful for system design. The study is purely empirical benchmarking with no derivations or fitted parameters, so its value rests entirely on the fairness and reproducibility of the experimental design.

major comments (2)
  1. [Experimental Setup] The experimental protocol trains every backbone under one fixed hyperparameter set (optimizer, schedule, dropout rates, epochs). Because deeper ResNets possess substantially higher capacity, this shared recipe can leave ResNet50 and ResNet101 under-optimized while shallower models may be over-regularized. Consequently the reported superiority of ResNet34 under background variation and ResNet50 under illumination variation may be an artifact of the common training recipe rather than a genuine depth-environment interaction. This assumption is load-bearing for the central claim that optimal architecture depends on the type of environmental variation.
  2. [Results] No error bars, multiple random seeds, or statistical significance tests are reported for the accuracy, confidence, and latency figures under the two environmental conditions. Without these quantities it is impossible to determine whether the observed differences (e.g., ResNet50 confidence ~0.869 versus other backbones) exceed run-to-run variability.
minor comments (3)
  1. [Abstract] Abstract: latency values are stated as 0.058-0.059 ms; clarify whether this is per-image, the batch size used, and the hardware platform on which timing was measured.
  2. [Abstract] Abstract: the phrase 'approaching or above 1.00' for accuracy should be replaced by an explicit statement of the metric (e.g., mean average precision at IoU=0.5 or top-1 classification accuracy) and the precise numerical range observed.
  3. [Dataset and Evaluation Protocol] The manuscript should state the size of the training and test sets, the exact procedure used to synthesize the illumination and background-contrast variations, and whether the same images were used across all backbone evaluations.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We have carefully considered each comment and provide point-by-point responses below, along with indications of revisions made to the manuscript.

read point-by-point responses

  1. Referee: [Experimental Setup] The experimental protocol trains every backbone under one fixed hyperparameter set (optimizer, schedule, dropout rates, epochs). Because deeper ResNets possess substantially higher capacity, this shared recipe can leave ResNet50 and ResNet101 under-optimized while shallower models may be over-regularized. Consequently the reported superiority of ResNet34 under background variation and ResNet50 under illumination variation may be an artifact of the common training recipe rather than a genuine depth-environment interaction. This assumption is load-bearing for the central claim that optimal architecture depends on the type of environmental variation.

    Authors: We thank the referee for highlighting this important aspect of our experimental design. The use of a single fixed hyperparameter configuration was intentional to enable a direct comparison of the backbones' inherent capabilities under identical training conditions, thereby isolating the effect of depth. This is a standard practice in empirical benchmarking to ensure reproducibility and fairness. Nevertheless, we recognize that deeper models could potentially achieve better performance with tailored hyperparameters. In the revised manuscript, we have included additional text in the Methods and Discussion sections to clarify this design choice and to discuss its implications for the generalizability of our findings. This revision qualifies our claims without altering the core observations. revision: yes

  2. Referee: [Results] No error bars, multiple random seeds, or statistical significance tests are reported for the accuracy, confidence, and latency figures under the two environmental conditions. Without these quantities it is impossible to determine whether the observed differences (e.g., ResNet50 confidence ~0.869 versus other backbones) exceed run-to-run variability.

    Authors: We agree that the absence of variability measures limits the ability to assess the statistical significance of the differences. Our experiments were performed with single training runs per backbone due to time and resource constraints typical in such benchmarking studies. In the revised manuscript, we have added a dedicated paragraph in the Results section acknowledging this limitation and explaining that the reported confidence differences are consistent and of sufficient magnitude to suggest they are meaningful. We also recommend multi-seed evaluations in future extensions of this work. revision: partial

Circularity Check

0 steps flagged

No circularity: purely empirical benchmarking study

full rationale

The manuscript reports results from training and evaluating four ResNet backbones inside RT-DETR under fixed training recipes and controlled lighting/background variations. No equations, derivations, fitted parameters renamed as predictions, or self-citations appear in the provided text. All reported quantities (accuracy, confidence, latency) are direct experimental outputs rather than quantities constructed from the authors' own definitions or prior claims. The central observation that intermediate-depth models balance performance differently under illumination versus background change follows from the tabulated measurements and does not reduce to any input by construction. The study is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

Empirical benchmarking study that relies on standard definitions of ResNet architectures and RT-DETR from prior literature; no new mathematical constructs or fitted parameters central to the claims.

axioms (2)
  • domain assumption ResNet backbones and RT-DETR detector behave as defined in their original publications
    Invoked when swapping backbones and assuming standard performance characteristics.
  • domain assumption Identical training configuration produces directly comparable models across different depths
    Central to attributing performance differences to depth rather than training artifacts.

pith-pipeline@v0.9.0 · 5559 in / 1202 out tokens · 46682 ms · 2026-05-12T00:52:47.732688+00:00 · methodology

discussion (0)

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