## 4 Experiments and Analysis
### Visualization Results
Here, the baseline method misses a substantial number of car targets, while the improved method detects more car targets when compared to the baseline. Moreover, the improved method also manages to detect the clock target that the baseline's results display, albeit both methods exhibiting some misclassifications of bus targets. This clearly demonstrates that the incorporation of the enhanced feature pyramid module enhances the detection capability for small-scale targets while maintaining accuracy for larger and medium-sized targets. Figure 9 provides a visual comparison of results on the VOC 2007 dataset. In the first row of results, the baseline method successfully detects the car target and the person driving the car. However, it misclassifies the billboard located next to the race track as a train target. Upon implementing the improved model, this misclassification is rectified. Shifting to the second row of results, the baseline method produces inaccurate detections, which are corrected when the improved model is utilized. Nevertheless, some misclassifications still persist, such as the incorrect labeling of a gift-wrapped target as a handbag target. Finally, in the third row of results, the baseline method encounters a notable misclassification scenario where a substantial number of fruit and biscuit targets are erroneously labeled as cake targets. Figure 10 provides a visual comparison of results on the VOC 2012 dataset. In the first row of examples, the baseline method successfully detects the dining table, person, and chair targets. However, the car detection result in the top right corner is incorrect. The improved model corrects this misclassification and identifies additional targets, such as mobile phones, blankets, bowls, and more chairs, showcasing superior performance compared to the baseline method. Transitioning to the second row of results, the baseline method misclassifies a distant car as a bus and erroneously labels a prominent piece of clothing as a person. These misclassifications are rectified in the output of the improved model. Moving on to the third row of results, in scenarios featuring smaller distant person targets, the improved model demonstrates improved detection performance compared to the baseline method, successfully capturing more of the smaller targets. Figure 9: Visualization results on VOC 2007 dataset. Here, we present a comprehensive analysis of the performance variations observed when applying our proposed method to different datasets. These variations are crucial for understanding the adaptability and limitations of our approach and shedding light on the intricate interplay of dataset characteristics, specific challenges, data distribution, model adaptability, and generalization capabilities. 1. Dataset characteristics: The COCO 2017 dataset and the COCO 2014 dataset differ in terms of the number of images, object categories, and scene complexity. COCO 2017 is more extensive, while COCO 2014 is a slightly smaller dataset. These differences in dataset characteristics can influence the performance due to variations in object diversity, object sizes, and scene complexities. The Pascal VOC datasets (2007 and 2012) are relatively smaller and contain fewer object categories. The smaller dataset size can make it more challenging for models to generalize, potentially affecting performance. 2. Data distribution: The distribution of object sizes and object densities within images can vary significantly between datasets. COCO datasets are known for their diverse object sizes and complex scenes, while Pascal VOC datasets may have different size and density characteristics. Our method, which takes into account object density using the gradient density loss, may show variations in performance based on these distributions. Class imbalance, where some classes have more instances than others, can also impact performance. Some datasets may exhibit more pronounced class imbalances than others, leading to variations in detection performance across object categories. 3.