# ODEM-GAN: An Object Deformation Enhancement Model Based on Generative Adversarial Networks
## 4 Experiments
### Ablation Studies  
We used our airdrop experiment dataset in the ablation studies. In the airdrop experiment, the parachute had severe deformation, so we set the parachute as the specific hard deformation object. The resolution of the image is scaled to \(800\times 800\) for training and testing in ablation experiments, and we use ResNet-50 as the backbone network and all networks based on the CBAF module [22]. We mainly evaluated the impact of adding a conditional generator and the positioning accuracy loss on the detector. When using the current feature patch as the condition of the generator and the generator does not add the condition, after using ODEM-GAN to enhance the training of the CBAF detection model, the CBAF detection model only has an AP score of 76.4% for parachute detection on the airdrop dataset (third row in Table 4), which is lower than the result of augmentation training without ODEM-GAN (second row in Table 4). When conditions are added to the generator, the AP score after enhanced training for the CBAF object detection model is 85.1% (the fourth row in Table 4), an increase of 0.3%. The experimental results show that the current feature patch must be used as the input of the generator to improve the parachute detection performance of the CBAF object detection model. The reason for this situation is analyzed in this paper: if conditions are not added to the generator, the hard samples generated by ODEM-GAN have no directionality, which will interfere with the learning of the classification and regression modules, resulting in a decrease in accuracy. In contrast, after the generator adds conditions, the generated hard deformation parachute samples are under specific conditions, have clear directionality, and can enhance the detection ability of the classification and regression modules. When the loss of positioning accuracy is added, after using ODEM-GAN to enhance the training of the CBAF model, the AP score of the CBAF model for parachute detection on the airdrop dataset is 88.4% (the fifth row in Table 4). The AP score increased by 3.6%. The experimental results show that when the generator adds the positioning accuracy loss, the generator will generate parachute deformation samples with clear position information and can effectively reduce the impact of bounding box jitter on the regression accuracy in consecutive frames. We use the intersection-over-union (IOU) of two adjacent frames as the evaluation criterion for bounding box jitter. If the size and position of the bounding boxes in the adjacent two frames of images change little, the overlap ratio of the bounding boxes in the two frames is high; otherwise, it is small. The experimental results of the bounding box IOU in adjacent frames are shown in Figure 11a,b, respectively. The experimental results show that the IOU in adjacent frames of the model without ODEM-GAN is only approximately 0.2; after the ODEM-GAN module is added, the IOU in adjacent frames is increased to approximately 0.78, and the jitter is effectively suppressed. After adding the deformation enhancement model, the detection effect of the detector on the parachute in the open parachute state is visualized, as shown in Figure 12a,b. Therefore, using these samples to enhance the training of the regression module can further improve the accuracy of the detector on the object. The comprehensive experimental results show that using ODEM-GAN to enhance the training of the CBAF model can effectively improve the robustness of the CBAF model to deformation parachute detection. The experimental results are shown in Figure 13. We found that there are both guided parachutes (the smaller parachute in Figure 13) and the main parachute which is not fully opened. The original RetinaNet can only detect the larger parachute (the first row); our proposed CBAF model is for the small and deformable guided parachute detection performance is not robust enough (the second row, the second column, the third column); and the ODEM-GAN model can detect the guide parachute and the main parachute well.