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

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.