## 4 Experiments and Analysis
### Comparative Experiments with Existing Methods  
To validate the practical effectiveness of the proposed enhancements, a series of comparative experiments were conducted by incorporating the proposed modules into the baseline method. Subsequently, these enhanced methods were systematically compared against several state-of-the-art object detection approaches, all evaluated on the four widely used benchmark datasets. The obtained validation results unequivocally demonstrate that the proposed method surpassed the performance of these existing advanced methods. Table 2 presents the performance comparison on the COCO 2017 dataset, where the enhancements proposed in this study were rigorously assessed against prominent 2021 methods, including YOLOX, YOLOF, and Deformable DETR. The observed improvements in average precision were substantial, with increments of 0.3%, 0.1%, and 0.4% for the aforementioned methods, respectively. Furthermore, Table 3 reveals the outcome of the enhancements on the COCO 2014 dataset. The proposed improvements exhibited remarkable average precision boosts of 0.7% and 0.6% when juxtaposed with the YOLOX and YOLOF methods proposed in 2021. Additionally, a 0.1% enhancement was achieved when compared to Deformable DETR. These comparisons illustrate the potency of the introduced enhancements in enhancing object detection accuracy. The superiority of the proposed method is evident from the results displayed in Table 4 on the Pascal VOC 2007 dataset. Against Deformable DETR, YOLOF, and YOLOX, the method introduced in this paper exhibited remarkable average precision enhancements of 0.4%, 0.6%, and 0.7%, respectively. Notably, in terms of accuracy for detecting large objects, the proposed method outperformed YOLOX by a substantial margin of 1.9%. Moreover, when benchmarked against Dynamic R-CNN, DETR, and CentriPetalNet, the enhancements showcased their prowess by delivering substantial average precision improvements of 2.4%, 2.3%, and an impressive 4.0%, respectively. This compelling comparison demonstrates the considerable advancements introduced by the proposed method across multiple evaluation metrics. \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Model** & **AP\({}_{\textbf{0.50},\textbf{95}}\)** & **AP\({}_{\textbf{0.5}}\)** & **AP\({}_{\textbf{0.75}}\)** & **AP\({}^{\textbf{a}}\)** & **AP\({}^{\textbf{m}}\)** & **AP\({}^{\textbf{l}}\)** & **AP\({}^{\textbf{l}00}\)** & **AR\({}^{\textbf{s}}\)** & **AR\({}^{\textbf{m}}\)** & **AR\({}^{\textbf{l}}\)** & **Inference** & **Training** \\  & & & & & & & & & & & & **(img/s) & **(s/iter)** \\ \hline Baseline & 0.384 & 0.560 & 0.414 & 0.206 & 0.409 & 0.540 & 0.578 & 0.340 & **0.621** & 0.772 & **27.5** & **0.2982** \\ Baseline + Improvement 1 & 0.388 & 0.566 & 0.418 & 0.209 & 0.413 & **0.547** & 0.579 & 0.341 & **0.621** & 0.782 & 26.6 & 0.3095 \\ Baseline + Improvement 2 & 0.385 & 0.564 & 0.413 & 0.208 & 0.407 & 0.540 & 0.579 & 0.342 & **0.621** & 0.781 & 26.7 & 0.3100 \\ Baseline + Improvement 1 + & **0.395** & **0.578** & **0.423** & **0.232** & **0.418** & 0.543 & **0.581** & **0.367** & **0.621** & **0.788** & 26.4 & 0.3141 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The results of the ablation experiments on the COCO 2017 dataset.