Research on the application of deep neural network in automatic targeting technology

doi:10.16568/j.0254-6086.2022s1006

Nuclear Fusion and Plasma Physics ›› 2022, Vol. 42 ›› Issue (s1): 125-130.DOI: 10.16568/j.0254-6086.2022s1006

• Nuclear Fusion Engineering • Previous Articles Next Articles

Research on the application of deep neural network in automatic targeting technology

XIA Li-qiong, CHEN Bo-lun, WANG Peng, WANG feng

(Laser Fusion Research Center, China Academy of Engineering Physics (CAEP), Mianyang 621900)

Received:2021-07-22 Revised:2022-01-28 Online:2022-05-15 Published:2022-05-23

深度神经网络在自动瞄靶技术中的应用研究

夏立琼，陈伯伦，王鹏，王峰*

(中国工程物理研究院激光聚变研究中心，绵阳 621900)

作者简介:夏立琼(1990-)，女，重庆人，硕士研究生，助理工程师，从事等离子体诊断系统控制技术研究。

Abstract

Abstract: Using the Mask R-CNN to segment the key structure of the CCDs image in inertial confinement fusion (ICF) experimental research, the center of the entrance hole is calculated as the position of the target, and the rationality and feasibility is verified with the simulated image data. Firstly, the program generates batches of simulated images with different structure parameters and different angles as a data set, secondly the operation of noise enhancement and position transformation are made to expand the samples, finally the algorithm model is trained and tested based on the samples, and the center of the entrance hole can be located as the position of the target. The test results show that the accuracy rate and recall rate are above 97%, and the target recognition accuracy is better than 10 pixels.

Key words: ICF, The binocular vision system, Mask R-CNN, Semantic segmentation

摘要： 基于Mask R-CNN模型，对惯性约束聚变实验研究中靶图进行关键结构的语义分割，通过计算中心来定位目标靶点位置，通过模拟靶图数据来验证算法的可行性。在算法验证过程中通过程序批量生成不同结构参数、不同角度的模拟靶图作为数据集，然后通过增噪、成像位置变换等操作拓展样本数量。在大样本数量基础上对算法模型进行训练测试，实现语义分割，通过计算注入孔的中心得到目标靶点位置。测试结果显示准确率和召回率均在97%以上，在靶点识别精度上优于10个像素点。

关键词: ICF, 双目瞄准, Mask R-CNN, 语义分割

CLC Number:

TL62+9

XIA Li-qiong, CHEN Bo-lun, WANG Peng, WANG feng. Research on the application of deep neural network in automatic targeting technology[J]. Nuclear Fusion and Plasma Physics, 2022, 42(s1): 125-130.

夏立琼, 陈伯伦, 王鹏, 王峰. 深度神经网络在自动瞄靶技术中的应用研究[J]. 核聚变与等离子体物理, 2022, 42(s1): 125-130.

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Research on the application of deep neural network in automatic targeting technology

深度神经网络在自动瞄靶技术中的应用研究

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