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.
Funder
the National Key Research and Development Program of China
the National Natural Science Foundation of China
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Cited by
2 articles.
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