Enhancing object detection in underwater aquatic system with YOLO-transformer hybrid model and IoT sensor integration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it