Ninth International Conference SmartCom 2025

Authors - U.Sakthi, Aman Parasher, Akash Varma Datla
Abstract - This work seeks to classify various ship categories on the high-resolution optical remote sensing dataset known as FGSC-23 using deep learning models. The dataset contains 23 types of ships, but for this study, six categories are selected: Medical Ship, Hovercraft, Submarine, Fishing Boat, Passenger Ship and Liquified Gas Ship. The adopted ship categories were thereafter used to train four deep learning models which included VGG16, EfficientNet, ResNet50v2, and MobileNetv2. The accuracy, precision, and AUC parameters were used to evaluate the models where the best one, the ResNet50v2, was set up as accurate. Using these models, it should be possible to achieve a practical deployment aiming at fine-grained classification of ships that will contribute to enhancing maritime surveillance techniques. ResNet50v2 model had the highest precision of 0.9058 and on the other hand MobileNetv2 had the highest AUC of 0.9932. The analysis of the identified models is performed further in this work to illustrate their advantages and shortcomings in adherence to fine-grained ship classification tasks. Based on this research, the practical implications transcend theoretical comparisons of performance metrics, as useful information is provided to improve security applications in the maritime domain, surveillance, and monitoring systems. Categorization and identification of ships is a very important process in going global maritime business because it is used in decision-making processes in fields like security and surveillance, fishing control, search and rescue and conservation of the environment. The models highlighted are namely ResNet50v2 as well as MobileNetv2, proved to be robust in real-time applications such scenarios because of their ability to accurately and proficiently distinguish the differences between the ship types. In addition, this study suggests the luminal possibility of doing further improvement on these models using data enhancement strategies like transfer learning, data augmentation, and hyperparameter optimization which would enable it to perform impressively on any other maritime datasets. Therefore, the outcomes are beneficial for furthering work in automated ship detection and classification and is important toward enhancing the overall effectiveness and safety of navies across the globe.