# ODEM-GAN: An Object Deformation Enhancement Model Based on Generative Adversarial Networks
## 4 Experiments
### Comparison to State of the Art  
We compare the ODEM-GAN module with other state-of-the-art detectors [32; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50] on the test-dev split. Through the experimental results, we found that the detector based on ODEM-GAN achieved 27.9% in APS, which is 2% better than our previously proposed CBAF model, as shown in Table 8, indicating that our model not only enhances the deformation target detection, but also improves the performance of small object detection compared with state-of-the-art detectors [29; 39; 41; 42; 45]. We visualized some experimental results, as shown in Figure 14, and we performed both deformation and small object detection with good performance; in Table 8, we previously proposed CBAF with no improvement in detecting larger objects, which is because the global features of large objects are destroyed due to the feature maps divide to feature patches in the feature map division layer, but on the ODEM-GAN model. There is a 1.5% improvement, which is due to the invariance of the samples generated by the GAN branch learned by the detector.  
In addition, DETR [41], DINO [43], GLIP [44], and DyHead [45] in Table 8 are all transformer-based object detection models, in which DINO and DyHead are based on the Swin-Transformer, although these algorithms implement higher general detection accuracy, because the transformer encoding method has a larger receptive field, which has a similar idea to our feature map division layer but needs to consume more training samples and consume more computing resources, resulting in lower operating rates. Although the QueryInst [46] algorithm has excellent performance in detection tasks, it is mainly used for instance segmentation, which is a query-based instance segmentation method. YOLOR-D6 [47] proposed a method to use semantic information in upper layers of CNNs to represent implicit knowledge and combine multiple discriminators for object detection, which has excellent performance and real-time performance. Comparing YOLOR-D6 and DINO, YOLOR-D6 makes full use of implicit knowledge, and DINO uses the transformer as a backbone, and its AP scores have been greatly improved, which is an improvement for ODEM-GAN as it provides a new direction for the future.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Object** & **RetinaNet** & **CBAF** & **ODEM-GAN** \\ \hline mAP & 73.8 & 75.1 & 76.8 \\ aero & 85.3 & 86.8 & 87.5 \\ bike & 79.2 & 80.6 & 82.8 \\ bird & 76.9 & 78.2 & 79.5 \\ boat & 59.9 & 62.1 & 66.2 \\ bottle & 50.8 & 51.6 & 58.1 \\ bus & 80.9 & 82 & 83.2 \\ car & 73.3 & 75 & 77.1 \\ cat & 93.9 & 94 & 94 \\ chair & 56.5 & 59.1 & 60 \\ cow & 80.1 & 81 & 83 \\ table & 60.2 & 62.1 & 62.5 \\ dog & 89 & 89.9 & 89.9 \\ horse & 85.8 & 87 & 88.5 \\ mbike & 84.8 & 85.4 & 85.9 \\ person & 78.6 & 80 & 82.3 \\ plant & 45.6 & 47.2 & 50.1 \\ sheep & 71.3 & 72.3 & 74.3 \\ sofa & 70.6 & 71 & 75.6 \\ train & 82.6 & 83.2 & 83 \\ tv & 70.9 & 72.7 & 74 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The performance based on the AP score of the ODEM-GAN module for each category on the PASCAL VOC 2012 dataset.  
Figure 14: Comparison between our improved detector and recent state-of-the-art detectors. From the first row to the last row, the detection results of YOLOv4, EfficientDet-2, DETR, RDSNet, RetinaNet and ODEM-GAN, respectively.