Enhancing object detection in underwater aquatic system with YOLO-transformer hybrid model and IoT sensor integration
Bibliographic record
Abstract
ABSTRACT Detection of submerged objects in underwater environments is uniquely challenging due to low visibility, variable light conditions, and high noise and distortion in Underwater Aquatic System (UAS). The traditional object detection models, including the most recent versions of You Only Look Once (YOLO), drop too often in accuracy and robustness under such hostile conditions. To handle these, YOLO-Transformer Hybrid model is proposed which combines YOLO's real-time detection capabilities with enhanced contextual understanding accorded by transformer-based attention mechanisms. The YOLO backbone extracts the essential features from an input image and processes the image through a number of convolutional layers for real-time object detection. It adds attention layers to the transformer module, focusing on relevant regions of the image to extract long-range dependencies and contextual relationships possibly missed by convolutional layers in isolation. Then augment this hybrid approach with Internet of Things (IoT) sensor data to add valued-added environmental context that may improve detection accuracy. IoT sensors can facilitate dynamic adaptation to the variations in underwater conditions by continuously providing real-time data on temperature, turbidity, etc. The proposed model ensures 97.5% detection accuracy, outperforming traditional YOLO models under challenging underwater scenes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".