# ODEM-GAN: An Object Deformation Enhancement Model Based on Generative Adversarial Networks
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Methods** & **Backbone** & \(\mathbf{AP}\) & \(\mathbf{AP_{50}}\) & \(\mathbf{AP_{75}}\) & \(\mathbf{AP_{\text{S}}}\) & \(\mathbf{AP_{\text{M}}}\) & \(\mathbf{AP_{\text{L}}}\) \\ \hline DINO [43] & ResNet-50 & 50.2 & 68 & 54.7 & 32.8 & 53 & 64.8 \\ YOLOOR-D6 [47] & YOLOv4-P6-light & 52.5 & 70.5 & 57.6 & 37.1 & 57.2 & 65.4 \\ YOLOv4 [35] & CSPDracher-53 & 41.2 & 62.8 & 44.3 & 20.4 & 44.4 & 56.4 \\ CPN [38] & DLA-34 & 41.7 & 58.9 & 44.9 & 20.2 & 44.1 & 56.4 \\ EfficientDet-D2 [39] & Efficient-B2 & 43 & 62.3 & 46.2 & 22.5 & 47 & 58.4 \\ DETR [41] & Transformer & 43.5 & 63.8 & 46.4 & 21.9 & 48 & 61.8 \\ SM-NAS: E2 [40] & - & 40 & 58.2 & 43.4 & 21.1 & 42.4 & 51.7 \\ CenterNet [49] & Hourglass-104 & 44.9 & 62.4 & 48.1 & 25.6 & 47.4 & 57.4 \\ \hline DINO [43] & Swin-T & 63.3 & 80.8 & 69.9 & 46.7 & 66 & 76.5 \\ DyHead [45] & Swin-T & 49.7 & 68 & 54.3 & 33.3 & 54.2 & 64.2 \\ GLIP [44] & Swin-T & 61.5 & 79.5 & 67.7 & 45.3 & 64.9 & 75 \\ QueryInst [46] & Swin-T & 49.1 & 74.2 & 53.8 & 31.5 & 51.8 & 63.2 \\ \hline RDSNet [42] & ResNet-101 & 38.1 & 58.5 & 40.8 & 21.2 & 41.5 & 48.2 \\ CBAF [22] & ResNet-101 & 40.9 & 60.2 & 43.2 & 22.5 & 43.3 & 50.4 \\ DyHead [45] & ResNet-101 & 46.5 & 64.5 & 50.7 & 28.3 & 50.3 & 57.5 \\ QueryInst [46] & ResNet-101 & 42.8 & 65.6 & 46.7 & 24.6 & 45 & 55.5 \\ our (RetinaNet + ODEM-GAN) & ResNet-101 & 41.3 & 60.5 & 43.2 & 24.8 & 43.3 & 51.4 \\ \hline Grid R-CNN w/FPN [48] & ResNeXt-101 & 43.2 & 63 & 46.6 & 25.1 & 46.5 & 55.2 \\ Dense RepPoints [37] & ResNeXt-101 & 40.2 & 63.8 & 43.1 & 23.1 & 43.6 & 52 \\ FoveBox [50] & ResNeXt-101 & 42.1 & 61.9 & 45.2 & 24.9 & 46.8 & 55.6 \\ RetinaNet [7] & ResNeXt-101 & 40.8 & 61.1 & 44.1 & 24.1 & 44.2 & 51.2 \\ CBAF [22] & ResNeXt-101 & 43 & 63.2 & 46.3 & 25.9 & 45.6 & 51.4 \\ DyHead [45] & ResNeXt-101 & 47.7 & 65.7 & 51.9 & 31.5 & 51.7 & 60.7 \\ QueryInst [46] & ResNeXt-101 & 44.6 & 68.1 & 48.7 & 26.6 & 46.9 & 57.7 \\ our (RetinaNet + ODEM-GAN) & ResNeXt-101 & 43.2 & 63.4 & 46.8 & 27.9 & 45.6 & 52.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results of the comparison of our improved detector (RetinaNet with our ODEM-GAN module) and state-of-the-art detectors. **Author Contributions:** Conceptualization, Z.Z. and Z.P. ; data collection, Z.T. ; analysis and interpretation of results, Z.Z. and F.G.; validation, Z.Z. and F.G.; writing--original draft preparation, Z.Z. and F.G.; writing--review and editing, F.G. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by the China Postdoctoral Science Foundation (2020M671597), Jiangsu Postdoctoral Research Foundation (2020Z100), the National Science Foundation of the Jiangsu Higher Education Institutions of China (20 KJB520002), Suzhou Planning Project of Science and Technology (No. SYG202024), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflict of interest.