# A Transfer Learning and Optimized CNN Based Maritime Vessel Classification System
## 1 Introduction  
The classification and identification of naval vessels are essential and challenging issues for the national defense and maritime security of coastal countries. These countries must constantly work to increase port efficiency and maintain a close eye on traffic to advance economically. Threats include illegal fishing, marine pollution, and piracy [1, 2]. Most of these issues require a global perspective because they are not specific to particular countries. Image technology has advanced in the last few years, so the ship classification method has taken center stage for identifying and classifying ship targets. The basic network structure of deep learning algorithms is the convolutional neural network (CNN), which has multiple layers for automatically learning key features from training data rather than complex manual feature extraction [3]. In particular, it has been reported to solve many complex tasks, such as image classification, image semantic segmentation, and image recognition and pattern analysis [4]. CNNs have recently been trained for image classification utilizing supervised learning on large-scale annotated datasets [5]. However, training image data requires the gathering of large amounts of data which is very hard and is expensive to run, especially in marine regions where there are few publicly available datasets [6, 7]. The maritime domain has a lot of datasets. Thesedatasets include object detection, tracking, and pirate detection [8], but they have not been used for maritime categorization. In the field of marine image classification, there are a few publicly available datasets, such as the fine-grained dataset (MARVEL) Maritime Vessels [9] and the VAIS dataset [10], which includes both visible and infrared images of ships. However, the limitations in these datasets, such as the unbalanced classes and poor image quality, could lead to incorrect image categorization [11]. In [12], [PERSON] et al. investigate a new deep convolutional neural network (DCNN). This model is considered the base model and is twice as small as the AlexNet model. The model is used for maritime vessel image classification and is helped by the advent of new processors with high processing capability and huge memory units. Additionally, with the availability of collected massive datasets [13], deep learning (DL) techniques such as DCNNs have been utilized to create vision-based classification algorithms that do not require manually created descriptors. Instead of manually constructing features, using raw photos as the input for learning may help deep learning models understand a hierarchy of different invariant properties for geometric alterations. Unprocessed data is supplied to a classification algorithm using deep learning techniques, automatically learning how to complete the classification task. The classification performance is frequently below average if the initial parameters are applied for a small image number of the classified target for CNN training [14]. Therefore, for smaller target datasets, available transfer learning techniques can be used [15]. Transfer learning (TL) is a helpful technique for initializing a massive network with many images and classes. For these kinds of problems, ImageNet is frequently employed as the source dataset. Then, using a sizable dataset, transfer learning substitutes a new fully connected layer for the fully connected layer (or layers) of a pre-trained network. Last, a smaller target dataset is only used for training. DL techniques have successfully trained cutting-edge CNNs for object recognition since these models perform well in a wide range of visual tasks [16]. Due to the lack of reliable methods and annotated datasets, coarse-grained image classification rather than fine-grained image classification was used as a solution [17]. Even though some of the best CNN models have been used to classify images of ships, the EfficientNetBX CNN model is not usually used with the MARVEL dataset to detect variant vessels [18].