Improved Object Detection using Data Enhancement method based on Generative Adversarial Nets. Issue 1 (March 2021)

• Record Type: Journal Article
• Authors: Ruiqiang, Zhang; Jiajia, Liu; Yu, Zeng; Kejia, Pan; Lin, Huang
• Is Part Of: Journal of physics. Volume 1827:Issue 1(2021)
• Publication Date: 2021-03
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1827/1/012178
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: In deep learning-based object detection, especially in face detection, small target and small face has always been a practical and common difficult problem due to its low resolution, blurred image, less information and more noise. In some applications, sensing image data is hard to collect, leading to limited object detection performance. In this paper, we investigate using a generative adversarial network model to augment data for object detection in images. We use generative adversarial network to generate the diverse objects based on the current image data. An improved generative adversarial network is added in the network and a new loss funtion is applied during the trianing process to generate diverse and high-quality traing images. Experiments show that images generated by generative adversarial network have higher quality than counterparts.