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REVIEW 3 major objections 7 minor 76 references

SonarT165: A Large-scale Benchmark and STFTrack Framework for Acoustic Object Tracking

T0 review · 3 major / 7 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read This paper proposes the first large-scale benchmark for underwater acoustic object tracking, SonarT165, and a tracker called STFTrack that outperforms existing general and lightweight trackers on it.

desk verdict SonarT165 is a real benchmark contribution, but the STFTrack SOTA claim is undermined by an uncontrolled training-data comparison and a test-set feedback loop that the paper itself acknowledges. read the letter →

arxiv 2504.15609 v1 pith:2LW6C5VF submitted 2025-04-22 cs.CV

Yunfeng Li , Bo Wang , Jiahao Wan , Xueyi Wu , Ye Li This is my paper · ORCID
classification cs.CV
keywords underwateracousticobjecttrackingsonarimagingbenchmarksingletemplatefusiontrajectorycorrectionKalmanfilterfrequencyenhancement
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper argues that underwater acoustic object tracking has been held back by the lack of a common, large-scale evaluation set, and it supplies one: SonarT165, with 330 sequences in square and fan sonar formats and 205K manually checked annotations. It then evaluates a broad set of existing single-object trackers on this benchmark and finds that their localization precision is high while their overlap-based success is low, which it reads as evidence that acoustic appearance variation is a distinct challenge. To meet that challenge, it introduces STFTrack, built on the LiteTrack pipeline, with a multi-view template fusion module, an optimal trajectory correction module, and frequency-based image and feature enhancement. The paper's central claim is that STFTrack achieves state-of-the-art results on SonarT165 among both general and lightweight trackers, at a small computational cost over its baseline.

What carries the argument

The central mechanism is the acoustic-response-equivalent pixel property: in forward-looking sonar images, a pixel's brightness equals the strength of the acoustic reflection, so the target region can be separated from background by binarization at a threshold derived from the previous frame. This property carries the OTCM, where the normalized pixel brightness response $R_{np} = \text{mean}(\text{extract\_patch}(x_m, B_c))$ (with $x_m$ the binarized search image) is multiplied with an IoU score between Kalman prediction and candidate boxes to reject suboptimal matches, and the IoB score then checks whether the chosen box is just a neighbor of the maximum-response box. The other load-bearing piece is MTFM, which builds a multi-view dynamic template from original and binary images, enhances it with channel and spatial attention, and fuses it with the fixed template using cross-attention layers, giving the tracker a spatio-temporal target representation that adapts as the target's acoustic signature changes.

What would settle it

Take the low-acoustic-reflection sequences from SonarT165 (attribute LAR) and compare full STFTrack against STFTrack with the OTCM brightness term $R_{np}$ removed: if the success rate gap between the two versions essentially vanishes, then the acoustic-response-equivalent pixel prior is not what carries the improvement, and the paper's central mechanism would be disconfirmed.

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Extended reading notes

Core claim

STFTrack's design is built on a property of acoustic imaging: the pixel value in a sonar image is proportional to the acoustic reflection intensity at that point, so targets appear as bright, well-edged regions against a dark background. The multi-view template fusion module (MTFM) exploits this by feeding both the original dynamic template and a binarized version of it through a shared backbone, applying spatial and channel attention, and then fusing the resulting multi-view dynamic template with the fixed template through a cross-attention-style layer. The optimal trajectory correction module (OTCM) uses the same property to repair the standard Kalman-filter trajectory: it scores candidate boxes by their normalized pixel brightness response, combines that with an IoU score against the Kalman prediction, and uses an intersection-over-box2 check against the maximum-response box to suppress suboptimal matches. Added to this are an acoustic image enhancement step that adds back double high-frequency content and a frequency enhancement module that learns separate high- and low-frequency feature branches. On SonarT165, the paper reports STFTrack-B reaching 59.2 SR and 90.8 PR, and STFTrack-S reaching 57.6 SR and 89.9 PR, surpassing the best general tracker and the best lightweight baselines.

Load-bearing premise

The load-bearing premise is that the target is brighter in the sonar image than its surroundings, so a binarization threshold taken from the previous frame picks out the true target region; the paper's own failure cases show this stops holding under low acoustic reflection and background interference.

Editorial extensions

If this is right

  • SonarT165 gives the underwater acoustic tracking community a common testbed of 330 sequences and 205K annotations, so future acoustic trackers can be compared on equal ground instead of on private or tiny datasets.
  • Because general trackers already score near 80 PR but below 60 SR, the benchmark isolates overlap accuracy, getting the full box right rather than just the center, as the open problem in acoustic tracking.
  • STFTrack's efficiency numbers, roughly 283 FPS on an RTX 3090 Ti and 25 FPS on an Orin NX for the S variant, indicate the approach can run on embedded hardware of the kind used on underwater vehicles.
  • The benchmark's attribute analysis points to specific unsolved challenges, including acoustic object crossover, small targets, out-of-view, and low acoustic reflection, that next-generation trackers will need to address.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper leaves implicit that SonarT165 being test-only could be addressed by releasing a training split; doing so might reduce the domain gap that currently forces the model to train on RGB and thermal data.
  • The acoustic-response-equivalent pixel prior likely transfers to other reflection-imaging modalities such as medical ultrasound, where brightness likewise encodes reflected intensity; the paper points to this connection but does not demonstrate it.
  • A concrete extension would be to make the OTCM binarization threshold adaptive per frame or learned, since the failure cases show the previous-frame threshold degrades under low acoustic reflection and background interference.
  • If SonarT165 becomes a standard underwater acoustic tracking testbed, evaluating square and fan sequences separately, as this paper does, would be a natural protocol because tracker behavior differs systematically between the two formats.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 7 minor

Summary. The paper introduces SonarT165, a benchmark for underwater acoustic object tracking (UAOT) containing 165 square-format and 165 fan-format sequences with 205K manual annotations, together with attribute annotations and evaluations of a wide range of general and lightweight trackers. The paper also proposes STFTrack, built on LiteTrack, with three components: a multi-view template fusion module (MTFM), an optimal trajectory correction module (OTCM), and a frequency enhancement module (FEM), plus an acoustic image high-frequency enhancement preprocessing step. STFTrack-B and STFTrack-S are reported to achieve state-of-the-art success rate (SR) on SonarT165 among general and lightweight trackers, respectively, in Tables III and IV.

Significance. If the benchmark and the comparisons are reliable, SonarT165 would be a valuable community resource for UAOT: it is substantially larger than the existing RGBS50 sonar test set, includes both square and fan sonar formats, and provides attribute annotations that expose concrete failure modes such as low acoustic reflection and background interference. The proposed tracking pipeline is lightweight, and the reported speed/parameter numbers in Table X suggest practical deployment potential. However, the central SOTA claim is currently not established as stated, because the training data used for STFTrack differs from that used for the baselines and because all ablations and hyperparameter choices are made on the test sequences. The benchmark contribution is therefore conditionally significant; it would become fully significant if the evaluation protocol were made controlled and a validation split were provided.

major comments (3)
  1. [Section V.A, V.B; Tables III, IV, V] The state-of-the-art claim is confounded by training data. STFTrack is trained in two stages on LaSOT, GOT10k, UATD, and LasHeR (Section V.A), with thermal images used to simulate acoustic binary images, while the general and lightweight baselines in Tables III and IV are evaluated directly without any indication that they received UATD, LasHeR, or sonar-like fine-tuning. Table V shows that adding UATD to the LiteTrack baseline already improves SR (fan 55.1 to 55.9, square 54.8 to 54.9), so the reported margins of STFTrack-B over ARTrackV2Seq (59.2 vs 57.4 SR) and STFTrack-S over LiteTrack-B8 (57.6 vs 55.0 SR) could be driven by domain-specific pretraining rather than by MTFM, OTCM, or FEM. To support the SOTA claim, the authors should retrain or fine-tune the strongest baselines on the same acoustic/thermal data, or alternatively present a strictly controlled comparison in which every component is ablated with identical training data and report the marginal gains of the proposed modules over such matched baselines.
  2. [Section V.D, Table IX; Section VI.B] The ablation protocol uses the test set for model selection, creating a test-set feedback loop. All ablations in Tables V through IX are reported on SonarT165-Fan and SonarT165-Square, which are the same sequences used for the final performance claim, and Table IX selects the final enhancement variant (High x2) based on these test scores; note that High x3 is better on the fan split (60.7 vs 60.3 SR) but worse on the square split (57.9 vs 58.1 SR). Section VI.B acknowledges that the benchmark contains only test sequences and no training or validation split. Consequently, the chosen configuration, and hence the reported gains, may be optimistically biased by test-set feedback. The authors should add a validation split (or cross-validation over sequences) and perform module selection and threshold tuning on it, then report test results once for the selected configuration.
  3. [Section IV.C, Eqs. (8), (12)-(14); Figure 14] The OTCM module depends on hand-set thresholds and on an acoustic-brightness assumption that the paper's own failure analysis shows is violated. The binarization threshold in Eq. (8) is fixed at 30, the brightness segmentation threshold in Eq. (13) is the average pixel value of the target in the previous frame, and the IoB decision threshold in Eq. (14) is fixed at 0.6, with no sensitivity analysis for any of these values. The underlying assumption that target regions are reliably brighter than the background is contradicted by the LAR and BI attributes and by the failure cases in Figure 14 (notably sequences 015 fan and 055 fan), where low reflection and background interference cause accuracy degradation or drift. The authors should provide a threshold sensitivity study, a learned or otherwise justified derivation of these thresholds, and an explicit evaluation of OTCM behavior on LAR/BI sequences.
minor comments (7)
  1. [Table I] The row for SonarT165 lists 300 sequences, but the text and the rest of the paper consistently state 330 sequences (165 square plus 165 fan); this inconsistency should be corrected.
  2. [Section V.A] The implementation details state 'Python 2.4.0 and Python 3.10'; this is likely a typo for PyTorch 2.4.0, and the two versions should be clarified.
  3. [Figure 4] The caption lists '(b)' twice; the fourth panel should be labeled '(d)' to match the references in the text.
  4. [Table IV] The first metric column is labeled 'AUC' in the lightweight comparison table but 'SR' elsewhere; the metric names should be made consistent across tables, or the difference should be explicitly defined.
  5. [Section III.B.1] The text refers to 'UOT00 [50] and [51]', which appears to be a typo for 'UOT100 [50] and VMAT [51]'; the citation formatting should be corrected.
  6. [Section V.D.1] The text says 'as shown in Table 7' when referring to the FEM ablation, but the table is numbered VII; the cross-reference should be fixed.
  7. [Figure 3 captions] The attribute labels in the caption use 'BC' and 'TBI' instead of the attribute names 'TBC' and 'BI' defined in Table II; these should be aligned.

Circularity Check

0 steps flagged · score 0.0 of 10

No construction-level circularity found; the SOTA claim is an empirical measurement on a new benchmark, and the flagged data/training issues are validity concerns, not definitional circularity.

full rationale

No construction-level circularity found. STFTrack's components are implemented as stated equations (acoustic image enhancement, FEM in Eqs. 3-6, MTFM in Eqs. 8-11, OTCM in Eqs. 12-14) and are evaluated on a newly collected sonar benchmark; they do not define their outputs in terms of the benchmark metrics or of each other by construction. The state-of-the-art claim is an empirical measurement on SonarT165, not a quantity forced by the benchmark definition or by a fitted parameter renamed as a prediction. Self-citations to RGBS50 [1] and UOSTrack [27] are used as dataset/baseline references and are not invoked as uniqueness theorems or as proof of optimality. The paper itself notes that the benchmark contains only test sequences (Section VI.B), and the comparison is not controlled for training data because STFTrack is trained on UATD and LasHeR while baselines use generic RGB pretraining (Section V.A); these are serious external-validity and fairness concerns, but they do not make any derivation equivalent to its own input. No equation in the paper reduces to its own assumption, and no fitted parameter is presented as an independent prediction.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claims rest on hand-chosen thresholds, a per-frame brightness heuristic, and domain-transfer assumptions from RGB and thermal data to sonar. No new physical entities are introduced. The benchmark's annotation quality is asserted but not measured.

free parameters (4)
  • Binarization threshold for dynamic template (thres in Eq. 8) = 30
    Hand-chosen threshold for binary(zd, thres=30) in the MTFM; no sensitivity analysis is provided.
  • IoB decision threshold (Eq. 14) = 0.6
    Hand-chosen threshold; if Im is larger than 0.6, the candidate box is treated as suboptimal and the max-response box is output. No sensitivity analysis is provided.
  • Number of high-frequency image enhancements = 2 (High x2)
    Selected from Table IX based on SonarT-Fan and SonarT-Square test results; High x3 is better on fan sequences but worse on square sequences, so High x2 is a compromise.
  • OTCM brightness segmentation threshold (Eq. 13) = dynamic (mean pixel value of target in previous frame)
    The binarization threshold is recomputed each frame from the previous target appearance; this heuristic is not justified and can fail when the previous frame is noisy or the target has low reflection.
assumptions (4)
  • domain assumption Sonar pixel values are proportional to acoustic reflection intensity of the target.
    Used to justify the binarization operations in Equations 8 and 13; stated in Section I and Section IV.B.
  • domain assumption A model trained on grayscale RGB and thermal images from LasHeR transfers to acoustic sonar images.
    Second-stage training uses LasHeR thermal images to simulate acoustic binary images; no quantitative evidence of domain alignment is given.
  • domain assumption Training on LaSOT, GOT10k, and UATD generalizes to the SonarT165 test distribution.
    First-stage training uses these datasets; no validation split from SonarT165 is used to verify transfer.
  • domain assumption Manual annotations in SonarT165 are consistent and correct.
    Section III.A.2 states that annotations are proofread, but no inter-annotator agreement or quality metric is reported.

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Pith. "Pith review of SonarT165: A Large-scale Benchmark and STFTrack Framework for Acoustic Object Tracking." pith.science (2026). https://pith.science/paper/2LW6C5VF

@misc{pith2026250415609,
  author       = {Pith},
  title        = {Pith review of: SonarT165: A Large-scale Benchmark and STFTrack Framework for Acoustic Object Tracking},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2LW6C5VF}},
  note         = {Machine review of arXiv:2504.15609}
}
read the original abstract

Underwater observation systems typically integrate optical cameras and imaging sonar systems. When underwater visibility is insufficient, only sonar systems can provide stable data, which necessitates exploration of the underwater acoustic object tracking (UAOT) task. Previous studies have explored traditional methods and Siamese networks for UAOT. However, the absence of a unified evaluation benchmark has significantly constrained the value of these methods. To alleviate this limitation, we propose the first large-scale UAOT benchmark, SonarT165, comprising 165 square sequences, 165 fan sequences, and 205K high-quality annotations. Experimental results demonstrate that SonarT165 reveals limitations in current state-of-the-art SOT trackers. To address these limitations, we propose STFTrack, an efficient framework for acoustic object tracking. It includes two novel modules, a multi-view template fusion module (MTFM) and an optimal trajectory correction module (OTCM). The MTFM module integrates multi-view feature of both the original image and the binary image of the dynamic template, and introduces a cross-attention-like layer to fuse the spatio-temporal target representations. The OTCM module introduces the acoustic-response-equivalent pixel property and proposes normalized pixel brightness response scores, thereby suppressing suboptimal matches caused by inaccurate Kalman filter prediction boxes. To further improve the model feature, STFTrack introduces a acoustic image enhancement method and a Frequency Enhancement Module (FEM) into its tracking pipeline. Comprehensive experiments show the proposed STFTrack achieves state-of-the-art performance on the proposed benchmark. The code is available at https://github.com/LiYunfengLYF/SonarT165.

Figures

Figures reproduced from arXiv: 2504.15609 by the authors.

Figure 1
Figure 1. When underwater visibility is sufficient (in figure (a)), vehicle can use [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Main introduction of the proposed SonarT165 benchmark. (a) Data collection platform in the pool. (b) Sequence level proportion of different objects. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Visualization of different attributes of the proposed SonarT165 benchmark. To show more intuitively the challenges they pose to the tracker, we show [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: Visualization of bounding box distribution. (a) represents the distribution of the first frame bounding box in the fan sequences. (b) represents the [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: The overall framework of STFTrack. We take SOT pre-trained Litetrack [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 7
Figure 7. Figure 7: Presentation of frequency enhancement module. [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]
Figure 8
Figure 8. Figure 8: Presentation of the proposed multi-view template fusion module [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]
Figure 10
Figure 10. Figure 10: The success plots, precision plots and normalized precision plots of the trackers. These trackers are STFTrack-B256, ARTrackV2Seq-B256 [PITH_FULL_IMAGE:figures/full_fig_p010_10.png]
Figure 11
Figure 11. Figure 11: The success rate, precision rate and normalized precision rate of STFTrack-B, STFTrack-S, LiteTrack-B8 [PITH_FULL_IMAGE:figures/full_fig_p010_11.png]
Figure 12
Figure 12. Figure 12: Visualized comparisons of STFTrack-B with LiteTrack [PITH_FULL_IMAGE:figures/full_fig_p012_12.png]
Figure 13
Figure 13. Figure 13: Visualization of heat maps of our STFTrack-B, STFTrack-S, and [PITH_FULL_IMAGE:figures/full_fig_p012_13.png]
Figure 14
Figure 14. Figure 14: Visualizion of failure cases of STFTrack-B with LiteTrack [PITH_FULL_IMAGE:figures/full_fig_p013_14.png]

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.