Material classification technology based on convolutional neural networks

Dailin Li; Guilei Li; Baojun Wei; Dan Yang; Ning Wang; Huafeng Zhu; Hao Ni

doi:10.1117/12.2521860

12 March 2019 Material classification technology based on convolutional neural networks

Dailin Li, Guilei Li, Baojun Wei, Dan Yang, Ning Wang, Huafeng Zhu, Hao Ni

Proceedings Volume 11023, Fifth Symposium on Novel Optoelectronic Detection Technology and Application; 1102331 (2019) https://doi.org/10.1117/12.2521860
Event: Fifth Symposium on Novel Optoelectronic Detection Technology and Application, 2018, Xi'an, China

Abstract

The contact measurement techniques are typically used in the field of object material classification. It has a lot of disadvantages, such as the complex operation and time-consuming. In this paper, a new non-contact object material identification method based on Convolutional neural networks (CNNs) and polarization imaging is proposed. Firstly, the relationship between the complex refractive index of object and the polarization information is simulated, and then the structure of the CNNs is constructed according to the specific conditions of the polarization imaging system. The accuracy of the identification method is measured by repeated test using 7 materials. The experimental results show that the CNNs model can quickly realize the object material classification with the polarization images, and the classification accuracy is above 92%.

Citation Download Citation

Dailin Li, Guilei Li, Baojun Wei, Dan Yang, Ning Wang, Huafeng Zhu, and Hao Ni "Material classification technology based on convolutional neural networks", Proc. SPIE 11023, Fifth Symposium on Novel Optoelectronic Detection Technology and Application, 1102331 (12 March 2019); https://doi.org/10.1117/12.2521860

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