# A Transfer Learning and Optimized CNN Based Maritime Vessel Classification System
## 4 Dataset and Classification Results
### MARVEL Dataset Experimental Result  
For generalizability, the classification experiment analysis was applied to another publicly available dataset, MARVEL [7]. The selected dataset is divided into 26 different common ship categories. This dataset could better distinguish the classification capabilities of various neural networks for inland ships, thus reflecting the detection performance when various classification networks were adjusted as the backbone network. This was conducted to ensure the generalizability of our proposed network. Cargo, military, cruise, carrier, and tanker were the five categories chosen. A total of 540 photos from each category was randomly chosen to serve as the test images for the proposed classification scheme.  
In order to accurately evaluate the capabilities of our system, we relied solely on the test images; the network was not retrained. Table 7 presents the classification precision achieved with this MARVEL dataset for our perusal.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class** & **Accuracy (\%)** & **Precision (\%)** & **Recall** & **Specificity** & **F1** \\  & & & **(\%)** & **(\%)** & **(\%)** \\ \hline Concatenation & & & & & \\ (Proposed) & 98.34 & 97.64 & 97.6 & 96.27 & 97.43 \\ \hline Confidence & & & & & \\ Averaging (Proposed) & 98.38 & 97.67 & 97.65 & 96.34 & 97.47 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluation metrics using different ensemble learning methods.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class** & **Accuracy (\%)** & **Precision (\%)** & **Recall** & **Specificity** & **F1** \\  & & & **(\%)** & **(\%)** & **(\%)** \\ \hline Cargo & 97.86 & 92.82 & 95.53 & 99.5 & 94.15 \\ Military & 99.17 & 99.98 & 100 & 99.93 & 99.52 \\ Cruise & 98.86 & 99.96 & 99.56 & 99.28 & 100 \\ Carrier & 100 & 100 & 99.37 & 82.65 & 99.04 \\ Tanker & 95.75 & 95.46 & 93.53 & 100 & 94.45 \\ \hline Overall & 98.34 & 97.64 & 97.6 & 96.27 & 97.43 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation metrics for the Game of Deep Learning Ship dataset.  
A better result was achieved, even though a different dataset was employed. The proposed ship classification approach performed well even when applied to a new dataset.  
To indicate the accuracy of different class ship classification, the confusion matrix for MARVEL ship classification dataset is shown in Figure 9. The accuracy values indicate the higher accuracy achieved with the integration of modified model and the optimization of hyper-parameters, then the ensemble learning for the best three models with confidence averaging.  
Table 7 shows the results of testing our proposed ensemble transfer learning ship classification system with the MARVEL public dataset [7]. Retraining is not used to measure the generalization performance. Table 7 shows that the suggested model outperforms a different dataset. Tanker Acc is 94.61%, whereas carrier Acc is 100%. Military Acc is 98.42%, Cargo Acc is 97.65% and Cruise Acc 94.5%. The total performance statistics show an overall 97.04 Acc, 96.1 Pre, 95.92% Rec, and 96.55% F1. The study's findings indicate a strong classification performance using a different dataset, proving the model's generalization capacity.