# ODEM-GAN: An Object Deformation Enhancement Model Based on Generative Adversarial Networks
## 1 Introduction
If we choose the parachute in the stable descent phase as the training sample, it is difficult for the detector to detect the parachute in the first three phases. Because the parachute is gradually opened, its violent shape change will cause the confidence score during the detection process to drop sharply, resulting in detection failure (the detection object is lost multiple times during the experiment). If a severely deformed object is detected in a continuous image sequence, the boundary box will be inaccurately positioned and severely jitter. Figure 2 shows that the jitter of the bounding box is extremely unfavorable for the subsequent analysis of parachute shape and aerodynamic layout. In order to address these issues, we propose ODEM-GAN, an object deformation enhancement model based on a generative adversarial network (GAN) for object detection. ODEM-GAN has two branches: one is the CBAF branch and the other is the GAN branch. The CBAF branch is used for small- and scale variation object detection, which divides the feature map into several patches by spatial coordinates, and each of these patches has a size of \(m\times m\). These patches are used as the input of the contextual merging layer, and the output of the contextual merging layer, regarded as contextual feature-patches, is used for classification and regression, as shown in Figure 3. The GAN branch uses contextual feature  
Figure 1: The process of parachute movement. patches to generate deformation features for enhancing the performance of classification and regression networks. We highlight our main contributions as follows:  
* We propose ODEM-GAN, an effective object deformation enhancement model based on GAN, which focuses on the irrelevance of object deformation and scale variability to improve the prediction of hard sample probabilities and coordinates. Figure 3: General concept of the proposed CBAF module. Figure 2: The detection of the continuous image sequence (timing: from left to right, top to bottom). * In ODEM-GAN, an online deformable sample creation module and an anchor-free box prediction layer are developed. The online deformable sample creation module is to deform and augment the dataset. The anchor-free box prediction layer not only avoids accurately aligning discrete spatial anchor boxes and ground-truth boxes but also reduces the amount of calculation. * We propose the positioning accuracy loss which reduces the bounding box's inaccurate positioning and jitter caused by the detection of severely deformed objects in a continuous image sequence. The hard deformed samples generated by GAN can improve the regression accuracy of the object detector. * The experimental results on the airdrop dataset show that our ODEM module has obvious advantages in object detection with severe deformation. Moreover, the experimental results on MS-COCO show that the ODEM module also has advantages for small target detection, while improving the baseline of small object detection. In addition, the method also provides a new idea for object detection to replace object tracking algorithms. The rest of this paper is organized as follows. Section 2 presents a review of related works. The main procedures and ODEM-GAN are studied in Section 3. Comparison performance experiments are given in Section 4. Finally, the conclusion is drawn in Section 5.