Study on Rapid Archival Technology of Bullets Based on Graph Convolutional Neural Network
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Published:2021
Issue:
Volume:
Page:
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ISSN:1062-3701
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Container-title:Journal of Imaging Science and Technology
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language:en
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Short-container-title:j imaging sci technol
Author:
Pan Shi-bo1,
Pan Di-lin2,
Pan Nan1,
Ye Xiao1,
Zhang Miaohan1
Affiliation:
1. Faculty of Civil Aviation and Aeronautics, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
2. Kunming Zhiyuan Measurement and Control Technology Co., Ltd., Kunming 650500, Yunnan, China
Abstract
Traditional gun archiving methods are mostly carried out through bullets’ physics or photography, which are inefficient and difficult to trace, and cannot meet the needs of large-scale archiving. Aiming at such problems, a rapid archival technology of bullets based on graph convolutional neural network has been studied and developed. First, the spot laser is used to take the circle points of the bullet rifling traces. The obtained data is filtered and noise-reduced to make the corresponding line graph, and then the dynamic time warping (DTW) algorithm convolutional neural network model is used to perform the processing on the processed data. Not only is similarity matched, the rapid matching of the rifling of the bullet is also accomplished. Comparison of experimental results shows that this technology has the advantages of rapid archiving and high accuracy. Furthermore, it can be carried out in large numbers at the same time, and is more suitable for practical promotion and application.
Publisher
Society for Imaging Science & Technology
Subject
Computer Science Applications,Atomic and Molecular Physics, and Optics,General Chemistry,Electronic, Optical and Magnetic Materials