TST-YOLO: TOKEN-SELECTIVE TWIN-ENHANCED YOLO FOR ROBUST UNDERWATER FISH DETECTION AND PHENOTYPE ESTIMATION ON AUVS
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2026
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| _version_ | 1866901990691307520 |
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| author | SIRIGINEEDI MANIKANTA , Dr. RAGHVENDRA KUMAR , Dr. R N V JAGAN MOHAN |
| author_facet | SIRIGINEEDI MANIKANTA , Dr. RAGHVENDRA KUMAR , Dr. R N V JAGAN MOHAN |
| contents | <p>Underwater fish detection remains difficult due to turbidity, color cast, motion blur, and the prevalence of small, fast, look-alike targets on embedded AUV hardware. We present TST-YOLO, a compact detector that combines four synergistic components: (i) a physics-aware digital-twin synthesizer that exposes the model to realistic water-optics shifts during training; (ii) a lightweight pre-enhancement fusion stage that mitigates color/contrast bias before inference; (iii) a token-selective transformer head that prunes and merges low-information tokens for AUV-grade efficiency; and (iv) a phenotype-guided auxiliary head that estimates lateral-line scale counts to regularize features toward biologically meaningful structure. Evaluated under identical training budgets on DeepFish and DePondFi’23, TST-YOLO improves mAP@50–95 by +6.1 and +6.0 points, respectively, over strong YOLOv8/YOLOv7 baselines, with +5.5 APS_SS gains on small targets. Confidence calibration also improves (ECE ↓34%, Brier ↓), while the token-selective head reduces transformer tokens by ≈30% at equal or better accuracy, cutting end-to-end Jetson latency by ~6%. Results are reported as 5× runs (mean±SD) with bootstrap confidence intervals, paired tests, and McNemar analyses. Beyond accuracy gains, the contribution is positioned at the systems level, demonstrating how domain-aware data synthesis, reliability-oriented evaluation, and token-efficient transformer design can be jointly integrated for trustworthy, resource-constrained intelligent perception. This emphasis on efficiency, robustness, and statistical rigor highlights the relevance of TST-YOLO as an information-technology solution for dependable autonomous sensing rather than a task-specific detector alone.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18666965 |
| institution | Zenodo |
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| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | TST-YOLO: TOKEN-SELECTIVE TWIN-ENHANCED YOLO FOR ROBUST UNDERWATER FISH DETECTION AND PHENOTYPE ESTIMATION ON AUVS SIRIGINEEDI MANIKANTA , Dr. RAGHVENDRA KUMAR , Dr. R N V JAGAN MOHAN Underwater Object Detection; Autonomous Underwater Vehicles (Auvs); Digital-Twin Augmentation; Token-Selective Transformer; Underwater Image Enhancement; Small-Object Detection; Phenotype (Lateral-Line) Estimation. <p>Underwater fish detection remains difficult due to turbidity, color cast, motion blur, and the prevalence of small, fast, look-alike targets on embedded AUV hardware. We present TST-YOLO, a compact detector that combines four synergistic components: (i) a physics-aware digital-twin synthesizer that exposes the model to realistic water-optics shifts during training; (ii) a lightweight pre-enhancement fusion stage that mitigates color/contrast bias before inference; (iii) a token-selective transformer head that prunes and merges low-information tokens for AUV-grade efficiency; and (iv) a phenotype-guided auxiliary head that estimates lateral-line scale counts to regularize features toward biologically meaningful structure. Evaluated under identical training budgets on DeepFish and DePondFi’23, TST-YOLO improves mAP@50–95 by +6.1 and +6.0 points, respectively, over strong YOLOv8/YOLOv7 baselines, with +5.5 APS_SS gains on small targets. Confidence calibration also improves (ECE ↓34%, Brier ↓), while the token-selective head reduces transformer tokens by ≈30% at equal or better accuracy, cutting end-to-end Jetson latency by ~6%. Results are reported as 5× runs (mean±SD) with bootstrap confidence intervals, paired tests, and McNemar analyses. Beyond accuracy gains, the contribution is positioned at the systems level, demonstrating how domain-aware data synthesis, reliability-oriented evaluation, and token-efficient transformer design can be jointly integrated for trustworthy, resource-constrained intelligent perception. This emphasis on efficiency, robustness, and statistical rigor highlights the relevance of TST-YOLO as an information-technology solution for dependable autonomous sensing rather than a task-specific detector alone.</p> |
| title | TST-YOLO: TOKEN-SELECTIVE TWIN-ENHANCED YOLO FOR ROBUST UNDERWATER FISH DETECTION AND PHENOTYPE ESTIMATION ON AUVS |
| topic | Underwater Object Detection; Autonomous Underwater Vehicles (Auvs); Digital-Twin Augmentation; Token-Selective Transformer; Underwater Image Enhancement; Small-Object Detection; Phenotype (Lateral-Line) Estimation. |
| url | https://doi.org/10.5281/zenodo.18666965 |