ODEM-GAN: An Object Deformation Enhancement Model Based on Generative Adversarial Networks

Object detection has attracted great attention in recent years. Many experts and scholars have proposed efficient solutions to address object detection problems and achieve perfect perfor-mance. For example, coordinate-based anchor-free (CBAF) module was proposed recently to predict the category and the adjustments to the box of the object by its feature part and its contextual part features, which are based on feature maps divided by spatial coordinates. However, these methods do not work very well for some particular situations (e.g., small object detection, scale variation, deformations, etc.), and the accuracy of object detection still needs to be improved. In this paper, to address these problems, we proposed ODEM-GAN based on CBAF, which utilizes generative adversarial networks to implement the detection of a deformed object. Specifically, ODEM-GAN first generates the object deformation features and then uses these features to enhance the learning ability of CBFA for improving the robustness of the detection. We also conducted extensive experiments to validate the effectiveness of ODEM-GAN in the simulation of a parachute opening process. The experimental results demonstrate that, with the assistance of ODEM-GAN, the AP score of CBAF for parachute detection is 88.4%, thereby the accuracy of detecting the deformed object by CBAF significantly increases.