# ODEM-GAN: An Object Deformation Enhancement Model Based on Generative Adversarial Networks
## 1 Introduction  
With large-scale datasets [1, 2], object detection based on convolutional neural networks (CNNs) has achieved impressive performance. These CNN-based methods are not only improving bounding box object detection [3, 4, 5], but also making progress on local correspondence [6, 7]. However, for some particular situations, such as small object detection, scale variation, and deformations, the traditional CNN-based methods do not perform very well. To address this problem, some scholars have proposed to build multi-level feature pyramids and expand datasets for improving the detection accuracy. The former is from the perspective of knowledge modeling, and the latter is from a data-driven perspective. Most state-of-the-art approaches are based on preset anchor-boxes [8, 9, 10, 11, 12, 13] or use the feature pyramid network (FPN) [14, 15, 16, 17, 5] module to solve small object detection and scale variation. Although these methods achieve good performance, there are still some inherent limitations. First, these methods tile a large number of artificially preset anchor boxes, and then predict the category of these anchor boxes. To obtain the high intersection over union (IOU) rate between anchor boxes and ground-truth boxes, they refine these anchor boxes one or more times, which will produce too many negative samples and considerable computational redundancy during the prediction process. Second, the anchor boxes have predefined scales and aspect ratios, which belong to discrete space. However, the ground-truth boxes belong to continuous space, so in object detection, there will be anchor boxesand ground-truth boxes that are difficult to accurately align, which will lead to greater loss regression. For small object detection and scale variation, using feature pyramids is the principal method. Small objects are mapped to high-resolution feature maps (the low-level feature maps of the feature pyramid) [17] as they have fine-grained appearance information [18; 19]. The feature pyramid is the basic module of object detection at different scales. In addition, the object deformation should also be taken into consideration during detection. A common method to solve deformation is to use a data-driven learning strategy, that is, to collect as many images in different states as possible, and use it to train the detector network to improve the performance of the detector [20; 21]. However, it is impossible to collect the state of the object all the time. Moreover, the cost required to collect and label a large amount of data is also inconceivable. To tackle these limitations, [PERSON] et al. proposed a coordinate-based anchor-free (CBAF) module, which can effectively detect rigid objects of small and scale variation [22]. However, it does not work well with deformation objects such as parachutes. The reason is that the basic principle of the object detection algorithm is to calculate the similarity of the feature on the search image. When the similarity is greater than the pre-set threshold, it is considered the same object. If the parachute is just thrown as the object, 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 the fully opened parachute is used as the target, although the parachute will no longer deform drastically afterwards, the algorithm can stably detect the target, but when the parachute is just thrown from the aircraft, the parachute is not fully opened, and the detection algorithm cannot detect the parachute. As shown in Figure 1a-d, the movement process of the parachute is divided into four stages: (1) The free-fall stage of the airdropped item leaving the aircraft (Figure 1a); (2) The stage of pulling out the main parachute from the umbrella bag (Figure 1b); (3) The stage of inflating the main parachute (Figure 1c); and (4) The stage of stable descent of the main parachute (Figure 1d).