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Classification of materials using a pulsed time-of-flight camera

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Abstract

We propose an innovative method of material classification based on the imaging model of pulsed time-of-flight (ToF) camera integrated with the unique signature that describes physical properties of each material named reflection point spread function (RPSF). First, the optimization method reduces the effect of material surface interreflection, which would affect RPSF and lead to decreased accuracy in classification, by alternating direction method of multipliers (ADMM). A method named feature vector normalization is proposed to extract material RPSF features. Second, according to the nonlinearity of the feature vectors, the structure of hidden layer neurons of radial basis function (RBF) neural network is optimized based on singular value decomposition (SVD) to improve generalization. Finally, the similar appearance of plastics and metals are classified on turntable-based measurement system by own design. The average classification accuracy reaches 93.3%, and the highest classification accuracy reaches 94.6%.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 41606219, 41776186), the Scientific Research Project of Beijing Educational Committee (No. KM 201910005027) and the Rixin Foundation of Beijing University of Technology. In addition, the author would like to thank the anonymous referee for invaluable comments and suggestions.

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  1. Faculty of Information Technology, Beijing University of Technology, Beijing, People’s Republic of China

    Shinan Lang, Jizhong Zhang, Yiheng Cai, Xiaoqing Zhu & Qiang Wu

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  1. Shinan Lang
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  2. Jizhong Zhang
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  3. Yiheng Cai
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  4. Xiaoqing Zhu
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  5. Qiang Wu
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Correspondence to Xiaoqing Zhu.

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Lang, S., Zhang, J., Cai, Y. et al. Classification of materials using a pulsed time-of-flight camera. Machine Vision and Applications 32, 30 (2021). https://doi.org/10.1007/s00138-020-01163-5

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  • DOI: https://doi.org/10.1007/s00138-020-01163-5

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