Smart Maritime Surveillance: Leveraging YOLO Detection and Blockchain traceability for Vessel Monitoring
Bibliographic record
Abstract
Abstract This paper presents a comprehensive study on utilizing artificial intelligence (AI) and advanced detection techniques for the study and monitoring of ships. The primary objective is to prevent various issues, such as ship intrusion detection, ship detection in satellite images, and ship detection in river images. To achieve this, the study proposes innovative methods; including enhancing the capabilities of YOLOv3 and YOLOv8 neural networks to improve the accuracy of ship detection. Additionally, the study leverages IoT technology for real-time tracking and integrates feature fusion modules for more effective information integration. A crucial aspect highlighted in this study is the necessity of controlling pollution caused by ships. By addressing this environmental concern, the study aims to contribute to the preservation of marine ecosystems and enhance maritime safety. The results of the study demonstrate a significant enhancement in detection accuracy, showcasing the potential of these advanced methods for efficient and reliable ship monitoring systems.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".