A Structured Review of Underwater Object Detection Challenges and Solutions: From Traditional to Large Vision Language Models

Edwine Nabahirwa; Minghua Zhang; Wei Song; Yi Fang; Zhou Ni

REVIEW 4 major objections 5 minor 1 cited by

A Structured Review of Underwater Object Detection Challenges and Solutions: From Traditional to Large Vision Language Models

T0 review · 4 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read A structured review argues that underwater object detection still lacks a complete solution, that DALL-E 3 synthetic images give only marginal YOLO11 gains, and that LVLMs localize well but hallucinate class names.

desk verdict Useful survey with two thin case studies; the quantitative claims about DALL-E 3 augmentation do not survive scrutiny, but the review half is honest and could stand after reframing. read the letter →

arxiv 2509.08490 v1 pith:YP4RKRBH submitted 2025-09-10 cs.CV cs.AI

Edwine Nabahirwa , Wei Song , Minghua Zhang , Yi Fang , Zhou Ni This is my paper · ORCID

classification cs.CVcs.AI

keywords underwaterobjectdetectionlargevision-languagemodelssyntheticdataaugmentationDALL-E3Florence-2LoRAfine-tuningimageenhancementdomainshift

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 is a structured review of underwater object detection (UOD), organized around a five-part taxonomy of challenges: image quality degradation, target-related issues, data-related problems, computational constraints, and detection-methodology limits. Its central argument is that existing UOD methods address parts of these challenges but not the whole, and that large vision-language models (LVLMs) are a promising but unproven direction. To support that argument it runs two case studies: adding 1,200 DALL-E 3 synthetic images to 7,600 real images changes YOLO11 results only slightly (mAP@50 from 0.793 to 0.796, recall from 0.714 to 0.736, precision from 0.805 to 0.780), while a LoRA-fine-tuned Florence-2 draws good bounding boxes yet hallucinates class names so badly that mAP and recall could not be computed. The review's contribution is the taxonomy plus a candid statement of what current LVLMs can and cannot do underwater.

What carries the argument

The carrying mechanism is the five-category taxonomy of UOD challenges, which structures the whole review and lets the authors match each challenge family to a solution family (enhancement and restoration, image synthesis, detection architectures, domain adaptation, and efficient fine-tuning). Two case-study pipelines carry the empirical weight: DALL-E 3 text-to-image and image-to-image generation followed by OpenCV enhancement and manual annotation, then YOLO11 training on the original versus combined datasets; and Florence-2 with LoRA applied to attention projection, linear, and convolution layers, fine-tuning 1,929,928 parameters (0.7076% of the model).

What would settle it

Retrain YOLO11 on the original and combined datasets many times with different random seeds and compare the spread of mAP@50 and recall; if the 0.003 and 0.022 gaps overlap across runs, the claimed synthetic-data benefit is not established. Separately, map Florence-2's misspelled class names to ground-truth labels and recompute mAP and recall; if the corrected metrics do not beat a no-fine-tuning baseline, the 'strong localization' claim loses its support.

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Extended reading notes

Core claim

On its own terms, the paper establishes a systematic map of UOD difficulties and shows where the field stands. It claims that the five challenge categories—image quality degradation, target-related issues, data-related challenges, computational and processing constraints, and detection methodology limitations—are best treated together, because solutions aimed at one category (such as enhancement before detection) can interact with another (such as artifacts that hurt small-object localization). The case studies carry the review's main empirical claims: LVLM-generated synthetic data can augment scarce underwater datasets, with Table 8 showing mAP@50 rising from 0.793 to 0.796 and recall from 0.714 to 0.736 at a cost in precision (0.805 to 0.780); and parameter-efficient LoRA fine-tuning of Florence-2 enables strong bounding-box localization for small underwater objects while producing misspelled, ungrounded class names that make standard metrics meaningless. The paper therefore presents LVLMs as a direction with demonstrated localization promise and equally demonstrated output-reliability problems, not as a finished solution.

Load-bearing premise

The case-study conclusions assume that the small Table 8 improvements from adding synthetic images are real effects of the data rather than run-to-run noise, and that Florence-2's qualitative bounding boxes demonstrate localization strength despite hallucinated class labels.

Editorial extensions

If this is right

If LVLM synthetic data is used to augment small underwater datasets, the expected effect is modest: Table 8 shows mAP@50 moving from 0.793 to 0.796 and recall from 0.714 to 0.736, with precision dropping from 0.805 to 0.780.
LoRA fine-tuning of Florence-2 on 8,800 images can produce strong bounding-box localization for small objects while updating only 0.7076% of the model parameters, making LVLM adaptation computationally feasible.
Class-name hallucination is the binding constraint for LVLM-based UOD: as long as outputs like 'echinullop' and 'starchin' replace correct labels, standard mAP and recall cannot be computed and practical deployment is blocked.
Real-time UOD with LVLMs remains unresolved; the paper points to efficient fine-tuning, prompt tuning, and lightweight architectures as the next steps rather than a demonstrated result.
The five-category taxonomy gives a shared vocabulary for matching a specific underwater failure (turbidity, small objects, class imbalance, latency, bounding-box overlap) to the solution families reviewed.

Reading between the lines

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

One testable reading of Table 8 is that the synthetic images teach YOLO11 to propose more candidate boxes (higher recall) at the cost of false positives (lower precision); per-class precision-recall curves for the four target classes would show whether the gain is concentrated in classes that are rare in the real data.
Because only single training runs are reported, the 0.003 mAP@50 gain could easily be seed noise; repeating both training conditions with several seeds and holding the real-image count fixed would settle whether the synthetic set itself, rather than simply more training images, drives the change.
Florence-2's misspelled labels look like a lexical or tokenization failure rather than a visual grounding failure, since localization is reportedly strong; a constrained decoding or label-mapping postprocessor could recover usable metrics and clarify whether the bottleneck is semantic hallucination or vocabulary coverage.
The taxonomy could double as an evaluation rubric for future UOD papers, requiring authors to state which challenge categories a method attacks and which it leaves untouched, which would make cross-paper comparison less fragmented.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Colleague,

What you should know: this is a structured review of underwater object detection with two original case studies bolted on. The review portion is competent and honest, but the empirical claims in the case studies are not supported by the numbers reported.

What's actually new: the five-category taxonomy is a re-organization of known material, not a new framing. The genuinely new bits are (1) a DALL-E 3 synthetic augmentation experiment with YOLO11 on RF100, and (2) a LoRA fine-tune of Florence-2 for underwater detection. The review of LVLMs in UOD is timely, and the discussion of hallucination in Florence-2 is honest.

What the paper does well: the survey chapters are well-organized, the citations look solid, and the limitations paragraphs are unusually candid. The authors explicitly admit the synthetic data gain is small and possibly insufficient, and they admit the Florence-2 evaluation was impossible due to hallucinated class names. That's good faith.

Soft spots: Table 8 is the load-bearing claim, and it is weak. The differences (mAP@50 0.793 vs 0.796) are within single-run noise; there are no seeds, no error bars, no significance test. Worse, the comparison is confounded: the combined condition changes both data source and dataset size (7,600 to 8,800), so any improvement could just come from having more training data. Their own discussion in Section 5.1.4 acknowledges the gap is small, which undercuts the earlier "highlight the strength" phrasing. The Florence-2 case study claims "strong localization capabilities" and that the model "excelled" at drawing bounding boxes, but the paper itself says hallucinated class names made mAP/recall impossible to compute. That leaves anecdotal evidence. To support the claim they would need a human-evaluation protocol or a metric that tolerates label noise, plus code and prompts.

The review's self-citations to prior works are fine; those are legitimate reviews. But the PSNR/SSIM claim in Section 5.1.4 has no reported values.

Bottom line: as a review, this is usable for newcomers. As an empirical paper, it is a feasibility demo, not a result. The authors are honest about the limits, so it deserves a serious referee—the case studies should be reframed as anecdotal, and Table 8 should be reported as preliminary without causal claims.

Recommendation: send it to peer review with instructions to require major revision on the case-study claims. I would not cite the synthetic-data result, but I might cite the survey if I needed a UOD overview.

Referee Report

4 major / 5 minor

Summary. This manuscript is a structured literature review of underwater object detection (UOD). It categorizes challenges into five areas—image quality degradation, target-related issues, data-related challenges, computational and processing constraints, and detection methodology limitations—and surveys solutions from traditional image enhancement and restoration through modern CNN-, transformer-, and hybrid-based detectors, culminating in a discussion of large vision-language models (LVLMs). The review contributes two original case studies: (1) augmenting a real underwater dataset (7,600 images) with 1,200 DALL-E 3 synthetic images and evaluating YOLO11 on the combined 8,800-image set, and (2) fine-tuning Florence-2 with LoRA on the same combined data. The paper claims that synthetic augmentation modestly improves YOLO11 detection metrics and that the LoRA-fine-tuned Florence-2 demonstrates strong localization, while also openly acknowledging limitations including the small performance gap and hallucinated class names.

Significance. If the empirical claims hold, the paper would provide a useful reference point for applying generative LVLMs and parameter-efficient fine-tuning to data-scarce UOD. The literature survey is broad and the taxonomy is sensible, and the authors are commendably explicit about the limitations of their own case studies. The main significance is therefore as a structured survey with two preliminary feasibility pilot studies; the quantitative conclusions of those pilots are not yet established at the level the paper asserts.

major comments (4)

[Section 5.1.3, Table 8] The central claim that adding 1,200 DALL-E 3 synthetic images improves YOLO11 detection is not supported by the reported numbers. The differences in mAP@50 (0.796 vs. 0.793) and mAP@50-95 (0.505 vs. 0.501) are within the typical single-run stochastic variation of YOLO training, while recall (0.736 vs. 0.714) and precision (0.780 vs. 0.805) move in opposite directions. No training seeds, variance estimates, confidence intervals, or significance tests are reported. In addition, the comparison is confounded: the combined condition changes both the data source and the dataset size (7,600 to 8,800 images), so any observed improvement could result from simply having more training images or altered class composition rather than from synthetic data specifically. To support the stated conclusion, the authors should report multiple seeds with mean and standard deviation and include an ablation that isolates the synthetic contribution at a matched dataset size (e.g., adding 1,200 real images or subsampling the combined set to the original size).
[Section 5.1.4] The discussion explicitly concedes that 'the limited number of synthetic images (1,200) might not have been sufficient to blend effectively with the larger real dataset (7,600).' This admission directly undercuts the stronger statement in Section 5.1.3 that the results 'highlight the strength of synthetic data augmentation.' As written, the paper is internally inconsistent about the strength of its own evidence. The conclusion should either be tempered to reflect that no reliable improvement was demonstrated, or the experimental evidence must be strengthened to justify the stronger claim.
[Section 5.2.3] The claim that the LoRA-fine-tuned Florence-2 'demonstrated strong localization capabilities' and 'excelled' at drawing bounding boxes is not supported by any quantitative evaluation. The paper itself states that hallucinated class names made it impossible to compute meaningful mAP or recall values. Without either a controlled qualitative protocol (e.g., a predefined set of images, independent human rating, or localization-only IoU computed independently of class labels), this positive characterization remains anecdotal. The authors should either provide such an evaluation or present the case study strictly as a feasibility demonstration with no claims about the strength of localization.
[Section 5.1.4] The paper claims that the quality of the enhanced synthetic images was 'rigorously evaluated using metrics such as PSNR and SSIM,' but no PSNR or SSIM values are reported anywhere in the manuscript. This makes the quality claim unverifiable. The authors should report the actual metric values and describe the reference images and protocol used, or remove the claim.

minor comments (5)

[Section 5.1, Fig. 6] The pipeline defines three paths—P1 (synthetic only), P2 (real only), and P3 (mixed)—but Section 5.1.3 only evaluates P2 versus P3. P1 is never used in the experiments; either evaluate the synthetic-only condition or remove it from the pipeline description to avoid confusion.
[Section 3.3.1 and Fig. 5] The abbreviation for Detection Transformer is written as 'DETR' in most places but as 'DeTR' in at least one instance and in the Fig. 5 caption. Please standardize the spelling.
[Appendix A.3, Eq. (A.3)] Equation (A.3) reads 'V = 255 · G / ||G||', which is confusing because G appears on both sides with different meanings. Please use distinct symbols for the raw Gaussian kernel and the normalized vignette mask, and clarify how the Gaussian kernel is computed from the image dimensions.
[Table 1] For the URPC entry, the dataset column lists five separate counts in parentheses, one per year/version, but the 'Image Number' column is empty; this is inconsistent with other rows and should be clarified either by giving a single aggregate count or by splitting the row by version.
[Throughout] There are minor typographical issues, including 'Moreso' for 'More so,' 'Incase' for 'In case,' and inconsistent capitalization of 'scallop' in Figure 8 and Table 7. A careful proofreading pass would resolve these.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the case-study conclusions are empirical comparisons, not derivations that reduce to their inputs.

full rationale

This paper is a structured review with two empirical case studies, not a derivation chain, so the circularity burden is low and no circular step is exhibited. The main claim comparing YOLO11 on the original RF100-based dataset versus the combined dataset (Table 8) is a direct training comparison; the combined condition does change both data source and dataset size, and the reported gains are small and unreplicated, but that is a statistical and experimental-confounding weakness, not a case where a prediction is equivalent to an input by construction. No parameter is fitted to a subset and then renamed as a prediction, and no equation in the paper reduces to another equation by definition. The Florence-2 case study asserts 'strong localization capabilities' from qualitative bounding-box examples while the paper itself states that hallucinated class names made mAP and recall uncomputable; this is anecdotal evidence and an internal tension, not definitional circularity. The references to the authors' own prior works ([14], [56], [78], [102]) are background citations in a literature review and are not used to justify the paper's empirical conclusions or to forbid alternative explanations, so they are not load-bearing self-citations. The PSNR/SSIM质量控制 claim is mentioned without reporting values, but those metrics are not used to derive the mAP comparison, so the absence of values is a reporting gap rather than a circular reduction. Overall, the honest finding is no significant circularity; the concerns belong to evidence quality and statistical rigor, not to circular reasoning.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

No new physical or model entities are introduced; the paper's novelty claims concern the application of existing generative models and PEFT methods. The main ledger entries are hand-set constants in the synthetic-image pipeline and unstated experimental hyperparameters.

free parameters (6)

Enhancement weights alpha, beta, gamma, delta = 0.9 each
Hand-set blending weights in Appendix A.1 (Eqs. A.1, A.2, A.4, A.5) that control tint, turbidity, scattering, and particle effects; no sensitivity analysis is given.
Gaussian blur kernel size = 15x15, sigma not reported
Chosen in Appendix A.3 (Eq. A.19) to soften synthetic images; the sigma value is not stated.
Tint color = (20, 50, 100)
Arbitrary BGR tint used in Appendix A.1 Eq. A.1 to simulate underwater color shift.
LoRA rank and layer targeting = not stated; 1,929,928 trainable params, 0.7076% of total
Hyperparameters in Section 5.2.2 determine the capacity of adaptation, but the rank is not reported.
Learning rate and epochs = lr=5e-6, 20 epochs
Training choices in Section 5.2.2 are stated without a search or justification.
DALL-E 3 prompts = not released
Prompt engineering is the main hand-tuned control over the synthetic data distribution in Section 5.1, but no prompts or seeds are provided.

assumptions (5)

standard math Gaussian convolution and LAB color transfer equations in Appendix A are standard and internally consistent.
The mathematical expressions for enhancement in Appendix A follow textbook image processing definitions.
domain assumption The Roboflow100 (RF100) underwater subset is a reliable reference benchmark with correct labels.
The case study uses RF100 as the real-data baseline in Section 5.1.2 and Table 7, trusting the benchmark's annotations and composition.
domain assumption DALL-E 3 images plus OpenCV effects are sufficiently close to the real underwater distribution for evaluating detection augmentation.
The entire synthetic augmentation experiment in Section 5.1 assumes these images transfer to real underwater scenes without a quantitative domain-gap analysis.
domain assumption Single-run mAP values are stable estimates of model performance.
Table 8 reports one training run per dataset condition with no variance or significance testing, yet the conclusion of improvement relies on these single numbers.
domain assumption The Florence-2 pretrained checkpoint 'Florence-2-base-ft' revision 'refs/pr/6' is a suitable starting point for LoRA adaptation to UOD.
Section 5.2.2 selects this checkpoint without comparing alternatives or analyzing its pretraining distribution.

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Cite this review

Pith. "Pith review of A Structured Review of Underwater Object Detection Challenges and Solutions: From Traditional to Large Vision Language Models." pith.science (2026). https://pith.science/paper/YP4RKRBH

@misc{pith2026250908490,
  author       = {Pith},
  title        = {Pith review of: A Structured Review of Underwater Object Detection Challenges and Solutions: From Traditional to Large Vision Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YP4RKRBH}},
  note         = {Machine review of arXiv:2509.08490}
}

read the original abstract

Underwater object detection (UOD) is vital to diverse marine applications, including oceanographic research, underwater robotics, and marine conservation. However, UOD faces numerous challenges that compromise its performance. Over the years, various methods have been proposed to address these issues, but they often fail to fully capture the complexities of underwater environments. This review systematically categorizes UOD challenges into five key areas: Image quality degradation, target-related issues, data-related challenges, computational and processing constraints, and limitations in detection methodologies. To address these challenges, we analyze the progression from traditional image processing and object detection techniques to modern approaches. Additionally, we explore the potential of large vision-language models (LVLMs) in UOD, leveraging their multi-modal capabilities demonstrated in other domains. We also present case studies, including synthetic dataset generation using DALL-E 3 and fine-tuning Florence-2 LVLM for UOD. This review identifies three key insights: (i) Current UOD methods are insufficient to fully address challenges like image degradation and small object detection in dynamic underwater environments. (ii) Synthetic data generation using LVLMs shows potential for augmenting datasets but requires further refinement to ensure realism and applicability. (iii) LVLMs hold significant promise for UOD, but their real-time application remains under-explored, requiring further research on optimization techniques.

Figures

Figures reproduced from arXiv: 2509.08490 by the authors.

**Figure 2.** Some of the Challenges Faced by UOD from DUO Dataset [17] [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** The relationship Between Image Enhancement, Restoration, Image Synthesis, and Robust Object [PITH_FULL_IMAGE:figures/full_fig_p017_3.png] view at source ↗

Figures from the paper (10 more)

**Figure 4.** Figure 4: Multi-Scale Feature Extraction and Fusion in Object Detection [PITH_FULL_IMAGE:figures/full_fig_p020_4.png]

**Figure 5.** Figure 5: The Pipelines of Various Object Detection Model Architectures [PITH_FULL_IMAGE:figures/full_fig_p022_5.png]

**Figure 6.** Figure 6: The Workflow Showing the effect of Synthetic data augmentation and Enhancement in Improving [PITH_FULL_IMAGE:figures/full_fig_p027_6.png]

**Figure 7.** Figure 7: DALL-E 3’s text-to-image and image-to-image generation paths produced clear and highresolution underwater images, however, the generated images appeared overly pristine and lacked the typical noise and disturbances found in real underwater scenes. To address this, ima…

**Figure 7.** Figure 7: Synthetic Data Generation Pipeline 5.1.1. Image Enhancement on DALL-E 3’s Generated Images In this work, image enhancement plays a critical role in bridging the gap between synthetic underwater images and real-world underwater scenes. OpenCV was utilized to apply subt…

**Figure 8.** Figure 8: showcases sample synthetic underwater images with improved visual quality and [PITH_FULL_IMAGE:figures/full_fig_p029_8.png]

**Figure 8.** Figure 8: Sample Synthetic Generated Underwater Images with desired Target Object Classes (echinus, [PITH_FULL_IMAGE:figures/full_fig_p030_8.png]

**Figure 9.** Figure 9: YOLO11 inference results on underwater images after training with a combined dataset, demon [PITH_FULL_IMAGE:figures/full_fig_p034_9.png]

**Figure 10.** Figure 10: The Florence-2 Architecture with Highlighted Layers Where LoRA is Applied [PITH_FULL_IMAGE:figures/full_fig_p037_10.png]

**Figure 11.** Figure 11: Fine-tuned Florence 2 Detection results Understanding Hallucination in Florence-2: Hallucination [139],[140] refers to a model’s tendency to generate outputs that are not grounded in the input data. In the case of Florence-2, hallucination manifested as misspelled cla…

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Why Domain Matters: Domain-Aware Benchmarking of Underwater Object Detection and Annotation Quality
cs.CV 2026-07 conditional novelty 6.0 of 10

Physically grounded domain labels for underwater images expose large, consistent gaps in both human annotation quality and detector mAP that aggregate metrics conceal.

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