## 4 Experiment and Analysis
Figure 9 is a selection of some noticeable differences in the visual detection results. Through visual comparison of the test graphs in different scenarios, we have demonstrated the advanced nature of the algorithm presented in this paper while also identifying some issues. Based on this, we have chosen to describe in detail certain situations in which the performance was good yet with unresolved issues, thus providing a meaningful background for future research. Specifically, we verified the algorithm using an image named \"Cars\" to test its recognition performance in complex backgrounds with multiple identical target objects overlapping and interacting. The experiments showed that the original YOLOv8 code and YOLOv8+PNOC missed some targets, while the algorithm proposed in the paper and YOLOv8+DDU performed better. We also tested the \"People & Car & Horse\" image to assess the recognition performance when cars are in the distant background and to test the recognition of both people and horses when they overlap and obstruct each other. The results show that although the four scenarios had different recognition accuracies for distant objects, they all showed good outcomes. However, in the case of obstruction between people and horses, the YOLOv8 original code and YOLO+DDU could not distinguish between the two types of objects. In contrast, YOLOv8+PNOC with attention mechanisms and the proposed algorithm were able to recognize features more comprehensively, but further optimization is needed to effectively recognize heavily obscured people. The verification of images such as \"Dog,\" \"Cows,\" and \"tv-monitor\" was based on the exploration of recognition performance for nonhuman faces and objects without prominent features. The experiments proved that the proposed algorithm performed well in nonhuman face categories, thus achieving high precision when successfully identifying  
Figure 9: Visualization results of the VOC dataset. objects. Choosing the \"Computer\" image for testing was done to verify how different algorithms performed when encountering objects not in the training set. The experiment showed that the proposed paper mistook a computer screen for a \"tv-monitor,\" which, given their similarities, is an understandable misclassification for objects not learned before. Overall, from a subjective visual evaluation, the proposed algorithm performed well in other scenario comparisons of the ablation experiment and unearthed some issues that provide meaningful directions for further research. The MAR20 dataset has the following characteristics [39]: (1) MAR20 is the largest military aircraft target recognition dataset with remote sensing images, including 3842 images, 20 types of military aircraft models, and 22,341 instances, and each target instance has two labeling methods--horizontal boundary box and directed boundary box. (2) Because all fine-grained categories belong to the aircraft category, different types of aircraft often have similar characteristics, thus resulting in the higher similarity between different types of targets. (3) Due to the influence of factors such as climate, season, illumination, occlusion, and even atmospheric scattering in the process of remote sensing image acquisition, there are large intraclass differences among the targets of the same model. This feature is consistent with our fine-grained application testing.