An expert system for privacy-preserving vessel detection leveraging optimized Extended-YOLOv7 and SHA-256
Bibliographic record
Abstract
Maritime data security plays a crucial role in in Navy and Coastal Areas, where the detection of vessels is sensitive as well as boundless and demands privacy preservation and accurate identification. While manual identification of vessels can be challenging, advancements in Cryptographic hash functions, Deep Learning technology, and image processing have simplified the task. However, existing techniques like YOLOv3, with its struggles in handling unusual aspect ratios, YOLOv5’s low mean average precision, and R-CNN’s increased complexity and lack of privacy preservation, motivate the need for an improved approach. In lieu of this, we propose an Extended-YOLOv7 model as a more effective detection solution due to its favorable characteristics like CSPNet, Feature Fusion Module (FFM), Spatial Pyramid Pooling (SPP), and Non-Maximum Suppression (NMS). Additionally, utilization of the gradient descent algorithm aims to optimize system performance. To ensure privacy preservation, our work employs the widely recognized and secure hashing algorithm SHA-256, which is extensively used for data security. The proposed system facilitates detecting vessel traffic in designated areas such as ports and harbours as well as enables real-time vessel detection and tracking for enhanced security and safety purposes. In addition to safeguarding sensitive data, our research addresses compliance with privacy regulations, mitigates the risks of data breaches, and upholds ethical considerations. With the integration of these driving factors, this work strives to elevate the security analysis of detected maritime vessels, foster a sense of trust and assurance, and promote the use of ethical data management techniques. The proposed model provides better performance than other state-of-the-art methods. Specifically, this is accomplished by achieving a 9.3% increase in Precision over YOLOv7.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".