[1]王 晨,汤心溢,高思莉.基于深度卷积神经网络的红外场景理解算法[J].红外技术,2017,39(8):728-733.[doi:10.11846/j.issn.1001_8891.201708010]
 WANG Chen,TANG Xinyi,GAO Sili.Infrared Scene Understanding Algorithm Based on Deep Convolutional Neural Network [J].Infrared Technology,2017,39(8):728-733.[doi:10.11846/j.issn.1001_8891.201708010]
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基于深度卷积神经网络的红外场景理解算法
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《红外技术》[ISSN:1001-8891/CN:CN 53-1053/TN]

卷:
39卷
期数:
2017年第8期
页码:
728-733
栏目:
出版日期:
2017-08-20

文章信息/Info

Title:
Infrared Scene Understanding Algorithm
Based on Deep Convolutional Neural Network
文章编号:
1001-8891(2017)08-0728-06
作者:
王 晨123汤心溢13高思莉13
1. 中国科学院上海技术物理研究所,上海 200083;2. 中国科学院大学,北京 100049;
3. 中国科学院红外探测与成像技术重点实验室,上海 200083
Author(s):
WANG Chen123TANG Xinyi13GAO Sili13
?1. Shanghai Institute of Technical Physics, Shanghai 200083, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China;
3. Key Laboratory of Infrared System Detection and Imaging Technology, Chinese Academy of Sciences, Shanghai 200083, China
关键词:
红外图像红外场景语义分割卷积神经网络
Keywords:
infrared imagesinfrared scenesemantic segmentationconvolutional neural network
分类号:
TP391.41
DOI:
10.11846/j.issn.1001_8891.201708010
文献标志码:
A
摘要:
?采用深度学习的方法实现红外图像场景语义理解。首先,建立含有4类别前景目标和1个类别背景的用于语义分割研究的红外图像数据集。其次,以深度卷积神经网络为基础,结合条件随机场后处理优化模型,搭建端到端的红外语义分割算法框架并进行训练。最后,在可见光和红外测试集上对算法框架的输出结果进行评估分析。实验结果表明,采用深度学习的方法对红外图像进行语义分割能实现图像的像素级分类,并获得较高的预测精度。从而可以获得红外图像中景物的形状、种类、位置分布等信息,实现红外场景的语义理解。
Abstract:
?We adopt a deep learning method to implement a semantic infrared image scene understanding. First, we build an infrared image dataset for the semantic segmentation research, consisting of four foreground object classes and one background class. Second, we build an end-to-end infrared semantic segmentation framework based on a deep convolutional neural network connected to a conditional random field refined model. Then, we train the model. Finally, we evaluate and analyze the outputs of the algorithm framework from both the visible and infrared datasets. Qualitatively, it is feasible to adopt a deep learning method to classify infrared images on a pixel level, and the predicted accuracy is satisfactory. We can obtain the features, classes, and positions of the objects in an infrared image to understand the infrared scene semantically.

参考文献/References:

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[4] Noh H, Hong S, Han B. Learning deconvolution network for semantic segmentation[C]//Proceedings of the IEEE International Conference on Computer Vision, 2015: 1520-1528.
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备注/Memo

备注/Memo:
收稿日期:2016-10-06;修订日期:2016-10-31.
作者简介:王晨(1989-),博士研究生,主要研究方向是图像处理与目标识别。E-mail:ilkame@sina.com。
基金项目:国家“十二五”国防预研项目,上海物证重点实验室基金(2011xcwzk04),中国科学院青年创新促进会资助(2014216)。
更新日期/Last Update: 2017-08-21