# A Transfer Learning and Optimized CNN Based Maritime Vessel Classification System
## 1 Introduction
Academics have been trying to figure out how to make a good classification system for a long time. While all the previous research has yielded promising classification outcomes, more study is required to compare and analyze various classification algorithms implemented using CNN on a dataset of visible light images. A new classification algorithm is required to further improve the precision with which ships on inland waterways can be predicted. This is for the purpose of averting inevitable collisions. As mentioned above, the following enhancements have been made to the proposed classification system to ease the restriction. A novel CNN design has been implemented to increase the precision of ship classification systems, initially. Based on an improved and optimized CNN model, transfer learning (TL), and ensemble learning (EL) approaches, this research proposes an innovative ship classification model for ITS. The classification system is used for inland waterway collision avoidance. Eight cutting-edge CNN models are utilized when training base learners, including Xception, VGG-16, ResNet-50, Inception V3, InceptionResNetV2, DenseNetl21, MobileNet, MobileNetV2, and EfficientNetB0. These models are trained using the ship classification dataset to classify and detect different kinds of ships. In our proposed classification system, the classification accuracy of CNN learning models and the hyper-parameters of the CNN models are tweaked using a simple bio-inspired algorithm called Particle Swarm Optimization (PSO). PSO is used as a hyper-parameter optimization (HPO) technique [19]. To further enhance the ship detection performance, the underlying CNN models are combined, utilizing the ensemble procedures of confidence averaging and concatenation. The suggested ITS framework's efficacy and efficiency are assessed using two innovative ship classification datasets. To ensure its generalizability, the improved CNN model has also been evaluated on a more generalized MARVEL dataset, consisting of over 10,000 pictures and a whole class of 26 distinct vessels. Different classification algorithms have been compared to the proposed ship classification systems. The following are the primary contributions that this study makes:  
1. First, a new convolutional neural network (CNN) architecture has been employed to improve the accuracy of ship classification systems. 2. For hyper-parameter-optimization HPO, a PSO is used to enhance ship classification accuracy. Further, a new method is used to classify ships successfully for inter-class identification by using CNN, transfer learning and ensemble learning. 3. The proposed optimized ensemble learning method is tested on two ship classification benchmark datasets. The experimental results show efficient intra-class and ship detection, and the model's performance is compared to existing state-of-the-art methods. The rest of the paper is organized as follows. Section 2 introduces the related work that uses ML and DL algorithms for ship detection and classification. Section 3 presents the proposed ensemble transfer learning classification system. The experimental setup and classification results are presented in Section 4. Finally, the conclusion and future work are presented in Section 5.