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Deep learning enhanced terahertz imaging of silkworm eggs development

  • Hongting Xiong
  • , Jiahua Cai
  • , Weihao Zhang
  • , Jingsheng Hu
  • , Yuexi Deng
  • , Jungang Miao
  • , Zhiyong Tan
  • , Hua Li
  • , Juncheng Cao
  • , Xiaojun Wu*
  • *此作品的通讯作者
  • Beihang University
  • Peking University
  • CAS - Shanghai Institute of Microsystem and Information Technology
  • University of Chinese Academy of Sciences
  • Huazhong University of Science and Technology

科研成果: 期刊稿件文章同行评审

摘要

Terahertz (THz) technology lays the foundation for next-generation high-speed wireless communication, nondestructive testing, food safety inspecting, and medical applications. When THz technology is integrated by artificial intelligence (AI), it is confidently expected that THz technology could be accelerated from the laboratory research stage to practical industrial applications. Employing THz video imaging, we can gain more insights into the internal morphology of silkworm egg. Deep learning algorithm combined with THz silkworm egg images, rapid recognition of the silkworm egg development stages is successfully demonstrated, with a recognition accuracy of ∼98.5%. Through the fusion of optical imaging and THz imaging, we further improve the AI recognition accuracy of silkworm egg development stages to ∼99.2%. The proposed THz imaging technology not only features the intrinsic THz imaging advantages, but also possesses AI merits of low time consuming and high recognition accuracy, which can be extended to other application scenarios.

源语言英语
文章编号103316
期刊iScience
24
11
DOI
出版状态已出版 - 19 11月 2021

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