## 4 Experiments and Analysis
### Comparative Experiments with Existing Methods
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Model** & **AP\({}_{\textbf{0.50.95}}\)** & **AP\({}_{\textbf{0.5}}\)** & **AP\({}_{\textbf{0.75}}\)** & **AP\({}^{\textbf{8}}\)** & **AP\({}^{\textbf{m}}\)** & **AP\({}^{\textbf{1}}\)** & **AP\({}^{\textbf{100}}\)** & **AR\({}^{\textbf{8}}\)** & **AR\({}^{\textbf{m}}\)** & **AR\({}^{\textbf{1}}\)** \\ \hline Faster RCNN [8] & 0.418 & 0.619 & 0.418 & 0.275 & 0.429 & 0.508 & 0.598 & 0.284 & 0.624 & 0.729 \\ Focal loss [19] & 0.416 & 0.614 & 0.420 & 0.267 & 0.425 & 0.511 & 0.596 & 0.280 & 0.618 & 0.722 \\ CornerNet [23] & 0.428 & 0.548 & 0.429 & 0.284 & 0.427 & 0.518 & 0.618 & 0.297 & 0.633 & 0.728 \\ ATSS [24] & 0.440 & 0.599 & 0.481 & 0.299 & 0.459 & 0.518 & 0.628 & 0.314 & 0.642 & 0.740 \\ carafe [25] & 0.428 & 0.358 & 0.438 & 0.315 & 0.485 & 0.499 & 0.618 & 0.328 & 0.649 & 0.735 \\ Cascade RCNN [9] & 0.471 & 0.642 & 0.474 & 0.308 & 0.402 & 0.575 & 0.631 & 0.425 & 0.709 & 0.815 \\ CenterNet [26] & 0.423 & 0.574 & 0.427 & 0.234 & 0.468 & 0.591 & 0.603 & 0.359 & 0.637 & 0.832 \\ AutoAssign [27] & 0.452 & 0.6558 & 0.499 & 0.305 & 0.496 & 0.557 & 0.651 & 0.417 & 0.729 & 0.834 \\ CentriPetal Net [28] & 0.485 & 0.644 & 0.483 & 0.308 & 0.524 & 0.599 & 0.658 & 0.428 & 0.708 & 0.851 \\ DETR [29] & 0.502 & 0.700 & 0.528 & 0.286 & 0.561 & 0.623 & 0.684 & 0.483 & 0.728 & 0.895 \\ Dynamic R-CNN [30] & 0.501 & 0.692 & 0.538 & 0.344 & 0.548 & 0.625 & 0.689 & 0.483 & 0.752 & 0.899 \\ Deformable DETR [31] & 0.521 & 0.703 & **0.548** & 0.354 & 0.554 & 0.672 & 0.699 & 0.481 & **0.755** & **0.910** \\ YOLOF [32] & 0.519 & 0.700 & **0.548** & 0.353 & 0.542 & 0.661 & 0.702 & 0.482 & 0.731 & 0.900 \\ YOLOX [33] & 0.518 & 0.701 & 0.547 & 0.351 & 0.561 & 0.655 & 0.700 & 0.472 & 0.751 & 0.895 \\ Ours & **0.525** & **0.708** & **0.548** & **0.358** & **0.568** & **0.674** & **0.704** & **0.485** & **0.755** & 0.899 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative experiments were conducted on the Pascal VOC 2007 dataset. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Model** & **AP\({}_{\textbf{0.50.95}}\)** & **AP\({}_{\textbf{0.5}}\)** & **AP\({}_{\textbf{0.75}}\)** & **AP\({}^{\textbf{8}}\)** & **AP\({}^{\textbf{m}}\)** & **AP\({}^{\textbf{1}}\)** & **AP\({}^{\textbf{100}}\)** & **AR\({}^{\textbf{8}}\)** & **AR\({}^{\textbf{m}}\)**  
Table 5 reveals the compelling performance of the proposed method on the Pascal VOC 2012 dataset. Compared to YOLOX, YOLOF, and Deformable DETR, the method introduced in this paper excelled, with average precision improvements of 0.2%, 0.7%, and 0.1%, respectively. When placed against Dynamic R-CNN, DETR, and CentriPetalNet, the proposed method boasted impressive enhancements of 1.9%, 2.4%, and a notable 3.7% in terms of average precision. The results illustrate that the proposed method not only exhibited slight yet consistent improvements in average detection precision and detection recall metrics for various object sizes when compared to advanced methods like Deformable DETR and YOLOX, but it also outperformed Dynamic R-CNN, DETR, and CentriPetalNet, reaffirming its superiority across diverse evaluation metrics. In summary, our characterization of \"superior performance\" is based on improvements over the baseline model's results within the specific domain of image object detection. 1. Benchmark and context: When referring to our results as \"superior performance\", we take into account the benchmark and context in which our evaluation is conducted. Our research aims to enhance image object detection methods, and our proposed techniques achieve significant improvements over existing approaches. While the absolute value of 51% AP may not seem extraordinarily high in a broader context, it represents a noteworthy advancement within the specific domain of image object detection, which typically involves numerous challenges, including diverse object sizes, scales, and varying degrees of complexity. 2.