## 3 Results and Discussions
### Ablation Tests with the Adapted Loss Function
These two no longer contribute to further improvements in average precision, seemingly in contrast to the conclusion we have drawn previously that the set of D and E alone is more efficient (than the combination of A, B, and C) in enhancing the predictive results. It is assumed that this may partly be related to the features of augmentation methods in these sets. Transforms in this combination either enrich the dataset or make targets more recognizable, while the new loss function serves to reset the weights for the boxes and diminish the influence of the ones with poor quality. Judging by the statistics, these two methods do not work sufficiently well when combined together. For the testing of the complete sets of methods, we incorporate the optional techniques (mixup and label smoothing) in the compound models with the selected augmentation sets. The results are shown as Tests M7 to M9, where the model with augmentation (D, E) achieves the highest mAP among the individual models that have been discussed. From the perspective of combinations of augmentation methods, this result is in contrast to the trend in the previous tests without the optional techniques. Scores for the model augmented with the sets (A, B, C) and the one with the sets (D, E) are both improved when the two techniques are employed, while this trend no longer applies to the case with all the augmentation sets (A, B, C, D, E) selected. Again, this observation indicates that the composite method consisting of a variety of techniques has complicated effects, where the contribution of each individual ingredient may seem to be suppressed by different degrees when combined with one another and may reach a certain bottleneck where simple addition of techniques can no longer raise the performance of the detector. To further verify the effects of the modified EIOU loss, we have repeated the tests with the different augmentation sets (M4 to M9) using the original EIOU loss function. The revised EIOU function holds an advantage over EIOU in the test accuracy for most of the cases, with an average mAP improvement of 0.9% for the six tests and a maximum improvement of more than 2%. These statistics are a good indication of the superiority of the proposed loss function when combined with the augmentation algorithms and other techniques. The AP scores for each type of target are illustrated in Figures 5-7 for a closer analysis of the different improvements. Compared to the CIOU loss, noticeable increases in AP (\(\geq\) 1%) produced by revised EIOU loss occur for the three categories that account for the smallest percentages in the training and validation data. It indicates that the new loss function is most constructive in raising the inference accuracies of the targets which appear least frequently and is capable of ameliorating the problem of the uneven data distribution to some extent. With the augmentation sets A, B, and C selected, the modified loss function provides remarkable AP improvements for lighthouses (+1.8%) and fishing boats (+2%) and moderate improvements for all the other categories except rail bar, showing a tendency generally similar to the previous case with the revised loss alone. The situation changes when the model is augmented with Groups D and E, where the test accuracy drops mostly due to the two categories (lighthouse and fishing boat). Prediction is further enhanced only when the optional techniques of mixup and label smoothing are employed. This observation may be attributed to the following factors: (1) There are only a total of two transform methods in the set (D, E). (2) This combination is associated with strong spatial distortions, which make the performance of the detector less stable when functioning along with other methods.