深度卷积神经网络在辐射环境下核废料检测中的应用

向伟; 史晋芳; 刘桂华; 徐锋

doi:10.11884/HPLPB201931.190220

深度卷积神经网络在辐射环境下核废料检测中的应用

doi: 10.11884/HPLPB201931.190220

向伟^{1, 2,},
史晋芳^{1, 2, ,},
刘桂华²,
徐锋²

1.
西南科技大学制造过程测试技术教育部重点实验室, 四川绵阳 621010
2.
西南科技大学特殊环境机器人技术四川省重点实验室, 四川绵阳 621010

基金项目:

国防科工局核能开发科研项目 [2016]1295

国家自然科学基金项目 11602292

国家自然科学基金青年基金项目 61701421

详细信息

作者简介:
向伟(1994—)，男，硕士研究生，从事深度学习、目标检测研究; 924717953@qq.com

通讯作者:
史晋芳(1977—)，女，副教授，从事机器视觉工程研究; 603071939@qq.com

中图分类号: X946
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- 文章访问数: 1404
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- PDF下载量: 44
- 被引次数: 3
出版历程
- 收稿日期: 2019-06-17
- 修回日期: 2019-09-04
- 刊出日期: 2019-11-15

Application of deep convolutional neural network in detection of nuclear waste in radiation environment

Xiang Wei^{1, 2
,},
Shi Jinfang^{1, 2
, ,},
Liu Guihua²,
Xu Feng²

1.
Key Laboratory of Technology for Manufacturing Process, Southwest University of Science & Technology, Mianyang 621010, China
2.
Special Environment Robotics Laboratory of Sichuan Province, Southwest University of Science & Technology, Mianyang 621010, China

摘要

摘要: 针对辐射环境下核废料检测准确率低的问题，提出一种基于深度卷积神经网络的辐射环境下核废料检测算法Dense-Dilated-YOLO V3。实验结果表明，Dense-Dilated-YOLO V3在不增加参数的情况下扩大了网络感受野，也有效避免图像信息的损失，同时能够在核辐射环境下提取到更多的目标细节特征，对辐射环境下目标检测的准确率可达93.29%，比原算法提高5.53%，召回率可达91.73%，提高了8.28%，有效解决了复杂辐射环境下核废料检测准确率低的问题，为辐射环境下核废料检测提供了新的途径。
- 深度学习 /
- 卷积神经网络 /
- YOLO V3 /
- 核废料 /
- 目标检测
Abstract: Aiming at the low accuracy of nuclear waste detection under radiation environment, this paper proposes a nuclear waste detection algorithm named Dense-Dilated-YOLO V3 based on deep learning convolution neural network. The experimental results show that Dense-Dilated-YOLO V3 increases the network receptive field without increasing the parameters, effectively avoids the loss of image information, extracts more detailed features of the target in the radiation environment, and accurately detects the target under radiation environment. The rate reached 93.29%, which was 5.53% higher than the original algorithm, and the recall rate reached 91.73%, with an increase of 8.28%. It solved the problem of low accuracy of nuclear waste detection under complex radiation environment, and has better detection effect. It provides a new approach for nuclear waste detection.
- deep learning /
- convolutional neural network /
- YOLO V3 /
- nuclear waste /
- target detection

HTML全文

图 1 Darknet-53网络结构

Figure 1. The structure of Darknet-53 network

下载: 全尺寸图片幻灯片

图 2 Dense-Dilated- YOLO V3结构图

Figure 2. The structure of Dense-Dilated-YOLO V3

下载: 全尺寸图片幻灯片

图 3 去噪预处理图像

Figure 3. Denoising preprocessing of image

下载: 全尺寸图片幻灯片

图 4 网络训练参数收敛折线图

Figure 4. Network training parameter of the convergence line graph

下载: 全尺寸图片幻灯片

图 5 YOLO V3和Dense-Dilated-YOLO V3检测效果对比图

Figure 5. Detection effect comparison chart of YOLO V3 and Dense-Dilated-YOLO V3

下载: 全尺寸图片幻灯片

表 1 网络性能结果对比表

Table 1. The comparison table of Network performance result

evaluation index	Accuracy/%	RECALL/%	IOU/ %	MAP/ %
YOLO V3	87.76	83.45	83.97	82.58
Dense-Dilated-YOLO V3	93.29	91.73	88.64	90.34

下载: 导出CSV

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