Convolutional neural networks-based anti-weapon detection for safe 3D printing

Giao N. Pham; Suk-Hwan Lee; Ki-Ryong Kwon

doi:10.1117/12.2519447

27 March 2019 Convolutional neural networks-based anti-weapon detection for safe 3D printing

Giao N. Pham, Suk-Hwan Lee, Ki-Ryong Kwon

Proceedings Volume 11050, International Forum on Medical Imaging in Asia 2019; 110501B (2019) https://doi.org/10.1117/12.2519447
Event: 2019 Joint International Workshop on Advanced Image Technology (IWAIT) and International Forum on Medical Imaging in Asia (IFMIA), 2019, Singapore, Singapore

Abstract

With the development of 3D printing technology anybody can print weapons with home 3D printer. In this paper, we would like to present an anti-weapon detection algorithm for safe 3D printing using the convolutional neural networks (CNNs) to prevent the printing of weapons in 3D printing industry. The proposed algorithm is based on training the D2 shape distribution of 3D weapon models by the improved CNNs. The D2 shape distribution of 3D weapon model is calculated from geometric features and points on the surface of 3D triangle mesh in order to construct a D2 vector. The D2 vector is then trained by the improved CNNs. The training and testing results show that the proposed algorithm is more accuracy than the conventional works and previous methods.

Citation Download Citation

Giao N. Pham, Suk-Hwan Lee, and Ki-Ryong Kwon "Convolutional neural networks-based anti-weapon detection for safe 3D printing", Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 110501B (27 March 2019); https://doi.org/10.1117/12.2519447

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