# A Transfer Learning and Optimized CNN Based Maritime Vessel Classification System
## 5 Conclusions  
In this paper, we developed an enhanced model architecture for classification that is based on transfer learning and ensemble learning to classify ships using optimized CNN models for inland waterways. As a result, the classification model for classifying ships  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Classification Method** & **Year** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **Specificity (\%)** & **F1 (\%)** \\ \hline [PERSON] et al. [37] & 2014 & 82.00 & - & - & - & - \\ [PERSON] et al. [10] & 2015 & 87.40 & - & - & - & - \\ [PERSON] et al. [38] & 2016 & 83.33 & - & - & - & - \\ [PERSON] et al. [9] & 2017 & 90.93 & 90.86 & 91.01 & 90.84 & 90.93 \\ [PERSON] et al. [39] & 2018 & 79.20 & 79.66 & 79.33 & - & 78.83 \\ [PERSON] et al. [22] & 2018 & 78.73 & - & - & - & - \\ [PERSON] et al. [25] & 2020 & 95.06 & 95.07 & 95.06 & 98.77 & 95.05 \\ [PERSON] et al. [26] & 2021 & 95.8 & 95.83 & 95.80 & 95.07 & 95.81 \\ Our method & 2022 & 98.38 & 97.67 & 97.65 & 96.34 & 97.47 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison between state-of-the-art algorithms.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classification Method** & **Cargo** & **Military** & **Cruise** & **Carrier** & **Tanker** \\ \hline [PERSON] et al. [37] & 80 & - & - & 93.30 & 72.70 \\ [PERSON] et al. [38] & 87.50 & - & - & 80.00 & 82.50 \\ [PERSON] et al. [25] & 88.26 & 98.38 & 98.38 & 98.79 & 91.50 \\ [PERSON] et al. [26] & 93.00 & 98.00 & 98.00 & 99.00 & 91.92 \\ Our method & 96.36 & 97.13 & 92.61 & 100 & 92.32 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Classification result of MARVEL with state-of-the-art algorithms.  
performed better. The model is trained with the public dataset \"Game of Deep Learning\". The dataset includes five classes: cargo, military, carrier, cruise, and tanker ships. Eight pre-trained models are utilized for training. The EfficientNetB0 model records the best accuracy. First, the best model architecture was enhanced by optimizing the model hyperparameters. PSO is applied for optimizing the model parameters that affect the performance. The model architecture is then modified to improve classification and inter-class performance. The three most effective models were chosen for further development based on ensemble learning. An augmentation-based data transformation technique is also suggested to convert ship image data for the CNN models.  
With a success rate of 98.38%, the ensemble learning model outperformed the competition. Specifically, we enhanced the system by adding a new classification block consisting of three fully linked layers followed by ReLU and a dropout layer. The proposed technique has a higher success rate than the best existing algorithms.  
Our ship classification system was used on the publicly available MARVEL dataset to gauge how effectively the suggested classification method can be generalized to other larger datasets. A degree of accuracy of 96.36% was found in this experiment, proving that the proposed method is effective. Finally, the system was compared to other existing algorithms for categorizing various ship classes in inland waterways, and our suggested solution outperformed the others. In further studies, the proposed method will be improved so that ships in different weather conditions may be classified for the modern vessel classification system that requires fast processing and small data samples. Compelling image preprocessing will look at how well the classification works for noisy and low-contrast images.  
Conceptualization, methodology and writing--original draft, M.H.S.; methodology, validation and supervision, Y.L.; formal analysis and review, Z.L.; review and editing, A.M.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is available in the manuscript.  
The authors declare no conflict of interest.