Ship-at-Sea Vessel Detection and Tracking with a Multi-Point Fuzzy Model for the Deep Learning Classification Process - Wei, Enping; Tan, Yong Chai; Tai, Vin Cent (International Journal of Intelligent Systems and Applications in Engineering, 2023)
Efficient detection and tracking of small-scale vessels in ships at sea hold significant importance for various maritime applications, including safety, security, and resource management. This research paper designed a framework to address these challenges by employing deep learning algorithms in combination with the innovative Fuzzy Multi-Point Tracking Probabilistic Classifier (FMPTPC) for precise vessel detection and tracking small-scale vessels in ships at sea. The proposed FMPTPC model. The primary integrates deep learning techniques for vessel detection and the introduction of the FMPTPC as a novel classifier for tracking. Deep learning, utilizing convolutional neural networks (CNNs), aids in the detection of vessels in complex maritime environments, while the FMPTPC enhances tracking accuracy by considering multiple data points and applying fuzzy logic for probabilistic classification. With the FMPTPC initial vessel detection, the FMPTPC refines tracking by probabilistically classifying and tracking vessels based on various imagery data acquired with vision at ships at sea. Through the utilized fuzzy rules deep learning architecture is trained and tested for the validation. The analysis of the results expressed that the proposed FMPTPC model achieves a higher accuracy of 0.99 for robust vessel detection and tracking, essential for applications such as search and rescue, fisheries management, and border security. This paper is widespread adoption of deep learning algorithms integrated with the FMPTPC in ship-at-sea environments, where small-scale vessel detection and tracking are essential. This integration represents a significant advancement in the fields of maritime security, surveillance, and resource management, promising improved safety and security in waters.