Share Email Print
cover

Proceedings Paper • new

Convolutional neural networks-based anti-weapon detection for safe 3D printing
Author(s): Giao N. Pham; Suk-Hwan Lee; Ki-Ryong Kwon
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper 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.

Paper Details

Date Published: 27 March 2019
PDF: 5 pages
Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 110501B (27 March 2019); doi: 10.1117/12.2519447
Show Author Affiliations
Giao N. Pham, Pukyong National Univ. (Korea, Republic of)
Suk-Hwan Lee, Tongmyong Univ. (Korea, Republic of)
Ki-Ryong Kwon, Pukyong National Univ. (Korea, Republic of)


Published in SPIE Proceedings Vol. 11050:
International Forum on Medical Imaging in Asia 2019
Feng Lin; Hiroshi Fujita; Jong Hyo Kim, Editor(s)

© SPIE. Terms of Use
Back to Top