Abstract
A terahertz (THz) passive imager with automatic target detection is an effective solution in the field of security inspection. The high-quality training datasets always play a key role in the high-precision target detection applications. However, due to the difficulty of passive image data acquisition and the lack of public dataset resources, the high-quality training datasets are often insufficient. The generative adversarial network (GAN) is an effective method for data augmentation. To enrich the dataset with the generated images, it is necessary to ensure that the generated images have high quality, good diversity, and correct category information. In this paper, a GAN-based generation model is proposed to generate terahertz passive images. By applying different residual connection structures in the generator and discriminator, the models have strong feature extracting ability. Additionally, the Wasserstein loss function with gradient penalty is used to maintain training stability. The self-developed 0.2 THz band passive imager is used to carry out imaging experiments, and the imaging results are collected as a dataset to verify the proposed method. Finally, a quality evaluation method suitable for THz passive image generation task is proposed, and classification tests are performed on the generated images. The results show that the proposed method can provide high-quality images as supplementary.
Highlights
Accepted: 8 February 2022Terahertz (THz) waves can penetrate clothing; compared to X-rays, its photon energy is relatively low, so it is non-ionizing and harmless to the biological tissue
In the field of security inspection, THz imaging can be divided into active imaging and passive imaging
For the hidden object detection task, the radiation brightness temperature between the human body and the hidden object always has a significant difference, so the target boundary is more obvious in the terahertz passive image, which is beneficial to the target detection task
Summary
Terahertz (THz) waves can penetrate clothing; compared to X-rays, its photon energy is relatively low, so it is non-ionizing and harmless to the biological tissue. Most of the existing data augmentation techniques involve random cropping, splicing, rotation and other operations on the original image during the training process [6,7] Such technologies cannot generate some unseen images and do not fundamentally solve the problem. The proposed model can generate high-quality THz passive images to augment the dataset. The main contribution of this paper is the first attempt to apply deep-learning technology to achieve low-cost terahertz passive image data augmentation, which aims to help the further application of terahertz passive imaging systems in the field of security inspection. This rest of paper is organized as follows.
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