## 4 Experiments and Analysis
### Visualization Results  
This subsection presents the visualization results obtained from the COCO 2017, COCO 2014, Pascal VOC 2007, and Pascal VOC 2012 datasets. Among them, Figure 7 provides a visual representation of the outcomes achieved by this work on the COCO 2017 \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Model** & **AP\({}_{0.50.95}\)** & **AP\({}_{0.5}\)** & **AP\({}_{0.75}\)** & **AP\({}^{\text{s}}\)** & **AP\({}^{\text{m}}\)** & **AP\({}^{\text{1}}\)** & **AP\({}^{\text{100}}\)** & **AR\({}^{\text{s}}\)** & **AR\({}^{\text{m}}\)** & **AR\({}^{\text{1}}\)** \\ \hline Faster RCNN [8] & 0.395 & 0.592 & 0.392 & 0.254 & 0.402 & 0.486 & 0.532 & 0.263 & 0.608 & 0.701 \\ Focal loss [19] & 0.394 & 0.589 & 0.389 & 0.251 & 0.397 & 0.485 & 0.530 & 0.251 & 0.610 & 0.698 \\ CornerNet [23] & 0.402 & 0.521 & 0.405 & 0.261 & 0.404 & 0.496 & 0.598 & 0.275 & 0.612 & 0.704 \\ ATSS [24] & 0.421 & 0.572 & 0.462 & 0.271 & 0.432 & 0.492 & 0.604 & 0.294 & 0.624 & 0.723 \\ carafe [25] & 0.401 & 0.331 & 0.418 & 0.295 & 0.465 & 0.472 & 0.592 & 0.301 & 0.624 & 0.712 \\ Cascade RCNN [9] & 0.458 & 0.621 & 0.450 & 0.281 & 0.381 & 0.551 & 0.612 & 0.402 & 0.698 & 0.792 \\ CenterNet [26] & 0.401 & 0.557 & 0.449 & 0.215 & 0.448 & 0.571 & 0.605 & 0.334 & 0.624 & 0.819 \\ AutoAssign [27] & 0.431 & 0.635 & 0.471 & 0.285 & 0.474 & 0.539 & 0.621 & 0.394 & 0.700 & 0.804 \\ CentriPetal Net [28] & 0.462 & 0.621 & 0.468 & 0.281 & 0.504 & 0.574 & 0.638 & 0.402 & 0.684 & 0.831 \\ DETR [29] & 0.475 & 0.675 & 0.501 & 0.263 & 0.541 & 0.682 & 0.659 & 0.462 & 0.705 & 0.876 \\ Dynamic R-CNN [30] & 0.480 & 0.672 & 0.512 & 0.322 & 0.524 & 0.602 & 0.664 & 0.462 & 0.730 & 0.870 \\ Deformable DETR [31] & 0.498 & 0.682 & 0.521 & 0.335 & 0.532 & 0.651 & 0.681 & 0.462 & 0.732 & 0.871 \\ YOLOF [32] & 0.492 & 0.688 & 0.521 & **0.342** & 0.528 & 0.642 & 0.681 & 0.468 & 0.710 & 0.870 \\ YOLOX [33] & 0.497 & 0.682 & 0.521 & 0.335 & 0.538 & 0.601 & 0.681 & 0.474 & 0.731 & 0.872 \\ Ours & **0.499** & **0.689** & **0.525** & 0.304 & **0.547** & **0.652** & **0.682** & **0.478** & **0.741** & **0.874** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative experiments were conducted on the Pascal VOC 2012 dataset. dataset. In the first row of visual comparison results, the baseline method succeeds in detecting human targets and three bird targets, albeit missing the handbag. However, with the improved model, the detection of the handbag is accomplished without any adverse impact on the detection of other objects. Moving to the second row of visual comparison results, the baseline method can spot train cars and traffic lights, yet it overlooks the small distant bird target. Interestingly, the improved model successfully captures this overlooked target. In the third row of visual comparison results, the baseline method incorrectly identifies the camera in the bottom left corner as a water bottle and also mistakes the charger for a cellphone. However, upon evaluation with the improved model, these misclassifications are effectively rectified. Figure 8 illustrates the visualization results of this work on the MS COCO 2014 dataset. In the first row of results, the baseline method successfully detects the occluded teddy bear target and the sliced banana target, yet it falls short in detecting another occluded banana target. The improved method's detection results, on the other hand, manage to successfully identify the occluded banana target. However, there are a few instances of misclassifications, such as pies being incorrectly labeled as a bowl and banana targets. Figure 8: Visualization results on MS COCO 2014 dataset. Figure 7: Visualization results on MS COCO 2017 dataset. Moving to the second row of results, the baseline method effectively detects the bus target and the fire hydrant target, but struggles with misclassifying several pillars behind the bus as humans. The improved method's results rectify this misclassification. In the third row, a scenario involving extremely small targets is presented, showcasing multiple car targets.