[1]王凯旋,任福继,倪红军,等.基于循环互相关系数的CGAN温度值图像扩增[J].智能系统学报,2022,17(1):32-40.[doi:10.11992/tis.202106036]
WANG Kaixuan,REN Fuji,NI Hongjun,et al.Image amplification for temperature value image based on cyclic cross-correlation coefficient CGAN[J].CAAI Transactions on Intelligent Systems,2022,17(1):32-40.[doi:10.11992/tis.202106036]
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WANG Kaixuan,REN Fuji,NI Hongjun,et al.Image amplification for temperature value image based on cyclic cross-correlation coefficient CGAN[J].CAAI Transactions on Intelligent Systems,2022,17(1):32-40.[doi:10.11992/tis.202106036]
基于循环互相关系数的CGAN温度值图像扩增
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
17
期数:
2022年第1期
页码:
32-40
栏目:
学术论文—机器学习
出版日期:
2022-01-05
- Title:
- Image amplification for temperature value image based on cyclic cross-correlation coefficient CGAN
- Keywords:
- infrared image; image amplification; cyclic cross-correlation coefficient; conditional generative adversarial network; convolution neural network; substation equipment; loss function; image processing; temperature recognition
- 分类号:
- TP181
- 摘要:
- 针对变电设备红外图像温度值样本少、不均衡等问题，本文提出了一种基于循环互相关系数的条件生成对抗网络(conditional generative adversarial network, CGAN)温度值图像扩增方法。根据图像相似度提出了循环互相关系数，改进了CGAN模型的损失函数；使用改进后的CGAN模型在原始温度值图像数据集的基础上进行图像扩增，得到了包含11种标签的新数据集；采用卷积神经网络对传统图像扩增方法、原始CGAN模型和改进的CGAN模型扩增的图像进行对比和测试。结果表明，改进的CGAN模型收敛速度更快，训练过程稳定，扩增的图像轮廓清晰、细节丰富，客观评价指标最高，温度值识别准确率达到99.4%，实现了图像扩增的目的。
- Abstract:
- To solve the problems of small sample size and imbalance of infrared image for substation equipment, a temperature image amplification method based on cyclic cross-correlation coefficient conditional generative adversarial network (CGAN) is proposed. The cyclic cross-correlation coefficient is proposed according to the image similarity, which improves the loss function of CGAN. Then the improved CGAN is used to amplify the original temperature image data set, establishing a new data set containing 11 labels. Then, the traditional image amplification method, the original CGAN and the improved CGAN are compared using the convolution neural network (CNN). The experiment demonstrate that the proposed CGAN model has faster convergence speed and stable training process, and the generated images have clear contour and rich details. The objective evaluation index of the proposed method is the largest, and the recognition accuracy of temperature value reaches 99.4%, which realizes the purpose of the image amplification.
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- 相似文献/References:
-
[1]王凯旋,任福继,倪红军,等.面向电力设备红外图像的温度值识别算法[J].智能系统学报,2022,17(3):617.[doi:10.11992/tis.202105043]
WANG Kaixuan,REN Fuji,NI Hongjun,et al.Temperature value recognition algorithm for the infrared image of power equipment[J].CAAI Transactions on Intelligent Systems,2022,17():617.[doi:10.11992/tis.202105043]
备注/Memo
收稿日期:2021-06-29。
基金项目:江苏高校优势学科建设工程项目（PAPD）；德岛大学研究集群项目（2003002）.
作者简介:王凯旋，硕士研究生，主要研究方向为电力设备缺陷检测和图像处理；任福继，教授，博士，日本工程院院士、欧盟科学院院士，中国人工智能学会名誉副理事长、日本工学会、IEICE、CAAIFellow，日本国际先进信息研究所的主席，主要研究方向为人工智能、情感计算、自然言语理解、模式识别。获吴文俊人工智能科学技术奖创新一等奖等，主持德岛大学研究集群项目，申请发明专利10余项。发表学术论文500余篇；倪红军，教授，博士，现任南通大学张謇学院院长，中国有色金属协会再生金属分会学术委员会委员，中国再生资源产业技术创新战略联盟理事，主要研究方向为新能源新材料及装备技术、人工智能。主持和参与科研项目40余项，申请发明专利70余项，获授权37项，成功转让发明专利14项。发表学术论文70余篇
通讯作者:倪红军. E-mail：ni.hj@ntu.edu.cn
更新日期/Last Update:
1900-01-01