An expert system for privacy-preserving vessel detection leveraging optimized Extended-YOLOv7 and SHA-256

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.