# A Transfer Learning and Optimized CNN Based Maritime Vessel Classification System
## 2 Related Work  
The primary object of ship classification is, of course, to allow for the most precise identification of ships possible. When monitoring is completed manually or using older techniques, mistakes are possible due to human error. Identifying the precise type of ship when looking at one among the many types of vessels that may be seen on a river can be challenging at times. Since this has already been shown, the problem is easy to solve by a reliable and effective network. Multiple factors, such as changes in weather (rain, snow, and fog), natural variations in sea level (waves), and other natural elements such as rocks, coral reefs, and sandbanks, as well as the size and form of the observed objects, make it difficult to detect and identify ships automatically. Numerous pieces of research have been conducted to solve these issues, leveraging profound advancements in deep learning techniques. The creation of boat categorization datasets, featuring photos of boats automatically retrieved by the ARGOS system, which is active in Venice during the day time, is described by [PERSON] et al. [20]. [PERSON] et al. [10] developed a gnostic field and a CNN that has already been trained on the ImageNet dataset. An accuracy of 82.4% was attained using only the gnostic field of the VAIS dataset. [PERSON] et al. [17] used lower and higher hierarchical level attributes to classify ship images. All accessible feature vectors were combined using DCNNs, and the output was then given to the support vector machine (SVM) classifier. They used a DL CNN to extract high-level properties and, in order to retrieve low-level attributes, they investigated Gabor filters. The accuracy rate for CNN fine-tuning with multi-scale rotation invariance properties is then demonstrated to be 88%. [PERSON] et al. [21] employed the ELM (Extreme Machine Learning) method to identify ships after training a CNN system on discriminative data. They pointed out that even when regularization techniques were used, there was still a risk of overfitting due to the paucity of training data. For the categorization of maritime vessels, [PERSON] et al. [22] used pre-trained CNNs in the ResNet and Inception architectural frameworks based on transfer learning and fine-tuning. Instead of constructing a CNN using random parameters, they were able to achieve classification by optimizing previously trained models using ImageNet. They achieved the highest accuracy of 78.73% on the Marvel dataset. [PERSON] et al. [23] investigated and compared various CNN-based ship recognition systems. [PERSON] et al. [24] described an alternative method. In this method, a multi-feature ensemble extracts data and is built on top of the AlexNet CNN, and a two-dimensional discrete fractional Fourier transform (2D-DFrFT) is also employed. In order to create multi-dimensional feature maps from the input picture, [PERSON] et al. [25] describe a deep feature extraction method based on TL that uses cascading CNN architecture. The MUTInf)-based feature selection algorithm's distributions make up a unique feature set that was created for a public ship dataset. The chosen classifier is a non-linear support vector machine (SVM) with a quadratic kernel. The suggested framework may incorporate the best feature set with a well-designed classifier to improve the effectiveness of the classification process in ship images. The proposed approach obtains an overall accuracy of 95.06% in the experiments. A CNN classification algorithm was introduced by [PERSON] [26] which can categorize five different types of ships in inland waterways, including tankers, cargo, military, carrier, cruise, and carrier ships. Based on experiments on the Game of Ship Classification dataset, the Resnet-152 pre-trained model produced the top model with an accuracy of 90.56%. In order to address the issue of a diminishing gradient, Resnet-152 was enhanced with the addition of a classification block with two fully connected layers, followed by ReLU, and a dropout layer.