# A Transfer Learning and Optimized CNN Based Maritime Vessel Classification System
## 4 Dataset and Classification Results
### Comparison to Related Works  
This section provides a contrast and comparison analysis of the proposed algorithm with several other existing approaches. Most studies on ship classification rely on a public dataset. A challenge remains, though, in standardizing the diverse research approaches that have been taken. Table 8 compares the overall performance of our inquiry with the most recent methodologies mentioned in the published literature. A system for classifying ships based on SAR photos and in situ data was developed by [PERSON] et al. [37]. This was found by using a backscattering-based categorization and the shape of the ship. It was 82% accurate.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class** & **Accuracy (\%)** & **Precision (\%)** & **Recall** & **Specificity** & **F1** \\  & & & **(\%)** & **(\%)** & **(\%)** \\ \hline Cargo & 97.65 & 86.29 & 93.65 & 98.99 & 89.81 \\ Military & 98.42 & 99.43 & 98.42 & 100 & 99.4 \\ Cruise & 94.5 & 100 & 93.9 & 99.69 & 98.14 \\ Carrier & 100 & 99.98 & 100 & 84.05 & 100 \\ Tanker & 94.61 & 94.81 & 93.61 & 100 & 94.21 \\ Overall & 97.04 & 96.1 & 95.92 & 96.55 & 96.31 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Classification performance for the MARVEL dataset.  
Figure 9: Confusion matrix for MARVEL dataset.  
A DL classification network based on CNN architecture with gnostic field technology was presented by [PERSON] et al. [8]. They used 0.2 for the gnostic field output and 0.8 for the CNN output. The system achieves an accuracy of 87.40%. Three types of ships were classified by [PERSON] et al. [38] based on their dispersion qualities. Their assignment was performed with a boat-length ratio of 83.33%.  
[PERSON] et al. [9] introduced an ML method; an SVM-classified CNN model is utilized to extract deep features. [PERSON] et al. [39] used a raw underwater audio stream to identify and classify ships in another study that was motivated by the auditory CNN model. The classification accuracy rating for five ship classes in the experiments came to 79.2%. The CNN-inspired auditory approach used by [PERSON] et al. [39] generated extensive analysis, but the five-class classification template produced subpar results. The Inception V3 network was employed with the transfer learning technique by [PERSON] et al. [22].  
Several learning rates were acquired to build an efficient classification network with higher accuracy. Using the modified EfficientNetB2 architecture, and adding a classification block with three fully connected layers, was chosen as the best of the three models for ensemble learning. Previous research and testing on a dataset from the standard ship datasets showed that our method worked better. Table 8 lists the performance indicators for the ship dataset [40].  
We compared the performance of our ensemble transfer learning with state-of-the-art ship classification methods for the MARVEL dataset. The metric rendition of the most modern techniques for each class of ship is shown in Table 9. Despite [PERSON] et al. [37] claiming the lowest accuracy in the tanker and cargo classes, [PERSON] et al. [38] demonstrated the most insufficient accuracy in the carrier class. The armed forces and cruise ships had no classes. Acceptable classification accuracy was attained by [PERSON] et al. [25] when compared to our suggested technique for the military and cruise ship classes. Still, poor classification accuracy was achieved compared to our suggested methodology for the other categories of ships. As can be seen from the results of the comparison, our proposed strategy outperformed the others.