Infrared Pedestrian Detection Based on GAN Data Augmentation

Abstract

Object detection, as an important branch of computer vision, has been widely studied in recent years. However, the lack of large labeled dataset obstructs the usage of convolutional neural networks (CNN) for detecting in thermal infrared (TIR) images. Most existing dataset focus on visible images, while thermal infrared images are helpful for detection even in a dark environment. To address this problem, we propose to use image-to-image translation models. These models allow us to translate the available labeled visible images to synthetic infrared images. Based on the original pedestrian dataset CVC-09, we use the pedestrian dataset CVC-14 to generate some labeled pedestrian infrared images. Finally, we compare original dataset with classic data augmentation and synthetic data augmentation training CNN. In addition, we explore the quality of synthetic TIR images using contrast experiments. The average precision of detection using classic data augmentation alone is 79.18%. By adding synthetic data augmentation, the average precision has improved to 82.24%. We believe that this method of synthetic data augmentation can be extended to other infrared detection applications and achieve other breakthroughs.