## 3 Results and Discussions
### Ablation Tests with the Adapted Loss Function
The optional techniques, in general, contribute positively to the classes with very few objects (\(\leq\) 5%) and may lead to a noticeable increase in AP for certain categories with low AP scores, e.g., cargo ship for M7, rail bar, and dock for M9, which are either easily confused with other classes or hard to detect and localize accurately. As a possible solution to the problem mentioned above, the approach of using a model ensemble can be employed to take advantage of the superiorities of models with different characteristics. The ensemble model comprising the best individual models (M7 and M9) has been constructed as our refined method. We also tested the performance of other ensemble models constructed with the existing frameworks shown in Tables 2 and 4. The relevant models, along with the scores, are demonstrated in Table 5. To be more specific,  
Figure 5: The average precision (AP) scores of the ten categories for the models with CIOU loss (Baseline 2), EIOU loss, and the revised EIOU loss. Results are obtained using NMS setting I.  
Figure 6: The average precision (AP) scores of the ten categories for Models A2 (augmentation A, B, C), M6 (A2 + revised EIOU), and M7 (A2 + revised EIOU + additional techniques). Figure 7: The average precision (AP) scores of the ten categories for Models A4 (augmentation D, E), M4 (A4 + revised EIOU), and M9 (A4 + revised EIOU + additional techniques). Ensemble 1 combines the effects of all the augmentation methods (with the CIOU loss) and the revised EIOU loss function. In test cases for Ensemble 2 and Ensemble 4, integration of the model using the augmentation sets (A, B, C) and the one using (D, E) is evaluated, while in Ensemble 3, the individual models differ in the settings for augmentation and additional techniques. The accuracy of the ensemble model surpasses the individual model that has a higher score in most of these cases, even when the discrepancy between the individual models is relatively large. Our proposed method, Ensemble Model 4, which benefits from the improved loss function and combines the superior strategies for data augmentation algorithms and other techniques, achieves the highest mAP of 52.6% (+4.3% compared to Baseline 1), which is higher than that of the best individual model. In order to perform a further analysis of the test accuracies of the models, we plotted a bar graph of the AP scores for each category produced with the baseline model (Baseline 1) and our methods (M7, M9, and Ensemble 4) on the test set in Figure 8. It can be seen when comparing the two single models with the highest accuracies to the baseline model that the accuracy for each category is enhanced in both of these two test cases. The AP value of the ensemble model lies either above the higher score or between the AP scores of the two relevant models. Boosts in the AP scores resulting from the ensemble model compared with the baseline are relatively stable among the categories, lying between +3% and +5% for most of the classes, except for fishing boat (+7.9%), lighthouse (+7.6%), and rail bar (+1.7%). In other words, the improvements and tricks adopted in this model have remarkable effects on the recognition of fishing boats and lighthouses and are shown to be least effective in detecting rail bars, the latter of which is one of the challenging problems of this dataset. Typical results generated by Ensemble Model 4 are presented in Figure 9 (2 nd column). Comparison with Baseline 2 (1 st column) indicates that the current model with the synthesized techniques overcomes many of the shortcomings of the results predicted by the baseline model via reducing incorrect annotations for the target category, removing some false detections of irrelevant or unrecognizable objects, and identifying targets missed by the previous model in certain circumstances, etc. However, the new algorithm might  
Figure 8: The average precision (AP) scores of the ten categories for the Baseline 1, M7, M9, and Ensemble 4 models.