A Novel Pattern for Infrared Small Target Detection With Generative Adversarial Network
Bin Zhao, Chunping Wang, Qiang Fu, Zishuo Han
Abstract
Since existing detectors are often sensitive to the complex background, a novel detection pattern based on generative adversarial network (GAN) is proposed to focus on the essential features of infrared small target in this article. Motivated by the fact that the infrared small targets have their unique distribution characteristics, we construct a GAN model to automatically learn the features of targets and directly predict the intensity of targets. The target is recognized and reconstructed by the generator, built upon U-Net, according the data distribution. A five-layer discriminator is constructed to enhance the data-fitting ability of generator. Besides, the L2 loss is added into adversarial loss to improve the localization. In general, the detection problem is formulated as an image-to-image translation problem implemented by GAN, namely the original image is translated to a detected image with only target remained. By this way, we can achieve reasonable results with no need of specific mapping function or hand-engineering features. Extensive experiments demonstrate the outstanding performance of proposed method on various backgrounds and targets. In particular, the proposed method significantly improve intersection over union (IoU) values of the detection results than state-of-the-art methods.