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Accurate Contact-Free Material Recognition with Millimeter Wave and Machine Learning

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Wireless Algorithms, Systems, and Applications (WASA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13472))

Abstract

Material recognition plays an essential role in areas including industry automation, medical applications, and smart homes. However, existing material recognition systems suffer from low accuracy, inconvenience (e.g., deliberate measuring procedures), or high cost (e.g., specialized instruments required). To tackle the above limitations, we propose a contact-free material recognition system using a millimetre wave (mmWave) radar. Our approach identifies materials such as metal, wood, and ceramic tile, according to their different electromagnetic and surface properties. Specifically, we leverage the following techniques to improve the system robustness and accuracy: (1) spatial information enhancement by exploiting multiple receiver antennas; (2) channel augmentation by applying Frequency Modulated Continuous Wave (FMCW) modulation; and (3) high classification accuracy enabled by Artificial Intelligence (AI) technology. We evaluate our system by applying it to classify five common materials. The experimental results are promising, with 98% classification accuracy, which shows the effectiveness of our mmWave-based material recognition system.

This work is supported by the National Key Research and Development Program of China (No. 2021YFB3100400), the Shandong Science Fund for Excellent Young Scholars (No. 2022HWYQ-038), the Guangxi Natural Science Foundation (No. 2020GXNSFBA159042).

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Authors and Affiliations

  1. Shandong University, Qingdao, Shandong, China

    Shuang He, Yuhang Qian, Huanle Zhang, Guoming Zhang, Minghui Xu, Xiuzhen Cheng & Pengfei Hu

  2. Bank of Jiangsu, Nanjing, China

    Lei Fu

  3. Fudan University, Shanghai, China

    Lei Fu

  4. Guangxi University of Science and Technology, Liuzhou, China

    Huan Wang

Authors
  1. Shuang He
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  2. Yuhang Qian
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  3. Huanle Zhang
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  4. Guoming Zhang
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  5. Minghui Xu
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  6. Lei Fu
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  7. Xiuzhen Cheng
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  8. Huan Wang
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  9. Pengfei Hu
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Corresponding author

Correspondence to Pengfei Hu .

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Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Lei Wang

  2. Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Michael Segal

  3. Chang Gung University, Taiwan, China

    Jenhui Chen

  4. Tianjin University, Tianjin, China

    Tie Qiu

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He, S. et al. (2022). Accurate Contact-Free Material Recognition with Millimeter Wave and Machine Learning. In: Wang, L., Segal, M., Chen, J., Qiu, T. (eds) Wireless Algorithms, Systems, and Applications. WASA 2022. Lecture Notes in Computer Science, vol 13472. Springer, Cham. https://doi.org/10.1007/978-3-031-19214-2_51

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  • DOI: https://doi.org/10.1007/978-3-031-19214-2_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19213-5

  • Online ISBN: 978-3-031-19214-2

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