| Citation: |
WEI Yongchao, LIU Qianqian, ZHU Hongchao, ZHU Zihan, LI Jin. Error Correction Algorithm of High Precision Temperature Measurement Based on EACN Model[J]. Infrared Technology , 2024, 46(7): 843-852.
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A temperature correction model, EACN, based on a channel attention mechanism is proposed to address the issues of insufficient accuracy and slow speed in temperature measurements from thermal imaging cameras. First, the model parameters are reduced by decreasing the features through 1x1 convolution. Second, we introduce a channel attention mechanism, ECA, to enhance the feature saliency expression between channels in the feature mapping module stage, compensating for lost feature information during dimensionality reduction and compression, thereby further improving the feature characterization capability of the model. Finally, through skip connections, shallow feature information is combined with semantic space information in the feature reconstruction stage, thus improving temperature correction accuracy. In this experiment, two data strategies were used on a self-built dataset. The experimental results show that the EACN model outperforms the SRCNN and VDSR models in both correction accuracy and speed.
| [1] |
胡蝶. 红外热像仪的测量技术及其应用研究[D]. 广州: 广东工业大学, 2019.
HU D. Research on Measurement Technology and Application of Infrared Thermal Imager[D]. Guangzhou: Guangdong University of Technology, 2019.
|
| [2] |
尤清涛. 浅谈红外热成像技术发展现状及未来发展趋势[J]. 中国安防, 2020, 172(5): 27-29. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGAF202005005.htm
YOU Q T. Discussion on the development status and future trend of infrared thermal imaging technology[J]. China Security, 2020, 172(5): 27-29. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGAF202005005.htm
|
| [3] |
王小丽, 王君, 严志勇. 影响红外测温精度原因分析[J]. 中国电力教育, 2010(S1): 559-560. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDI2010S1239.htm
WANG X L, WANG J, YAN Z Y. Analysis on the influence of infrared temperature measurement accuracy [J]. China Electric Power Education, 2010(S1): 559-560. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDI2010S1239.htm
|
| [4] |
刘纯红, 吴海滨, 熊丹枫, 等. 热辐射测温系统中探测器非线性校正方法[J]. 量子电子学报, 2017, 34(2): 227-230. https://www.cnki.com.cn/Article/CJFDTOTAL-LDXU201702014.htm
LIU C H, WU H B, XIONG D F, et al. Nonlinear correction method of detector in thermal radiation temperature measurement system[J]. Chinese Journal of Quantum Electronics, 2017, 34(2): 227-230. https://www.cnki.com.cn/Article/CJFDTOTAL-LDXU201702014.htm
|
| [5] |
王军帅, 田军委, 张杰, 等. 一种红外测温的误差建模与补偿方法[J]. 西安工业大学学报, 2021, 41(1): 40-45. https://www.cnki.com.cn/Article/CJFDTOTAL-XAGY202101008.htm
WANG J S, TIAN J W, ZHANG J, et al. An error modeling and compensation method for infrared temperature measurement[J]. Journal of Xi'an University of Technology, 2021, 41(1): 40-45. https://www.cnki.com.cn/Article/CJFDTOTAL-XAGY202101008.htm
|
| [6] |
CHEN Deyin, ZAN Tao, MA Zhiqian, et al. Research on pattern recognition performance of control chart based on deep learning[C]//Proceedings of 2022 Global Conference on Robotics, Artificial Intelligence and Information Technology, 2022: 212-216.
|
| [7] |
翟子洋, 畅宏达, 董世浩, 等. 修正环境因素对红外测温模块精度影响的分析[J]. 科学技术创新, 2021(24): 92-93. https://www.cnki.com.cn/Article/CJFDTOTAL-HLKX202124042.htm
ZHAI Z Y, CHANG H D, DONG S H, et al. Analysis of the influence of modified environmental factors on the accuracy of infrared temperature measurement module[J]. Science and Technology Innovation, 2021(24): 92-93. https://www.cnki.com.cn/Article/CJFDTOTAL-HLKX202124042.htm
|
| [8] |
李贞, 魏勇. 基于BP神经网络的红外测温补偿算法研究[J]. 机械制造与自动化, 2023, 52(1): 170-172, 176. https://www.cnki.com.cn/Article/CJFDTOTAL-ZZHD202301042.htm
LI Z, WEI Y. Research on infrared temperature measurement compensation algorithm based on bp neural network[J]. Machine Building & Automation, 2023, 52(1): 170-172, 176. https://www.cnki.com.cn/Article/CJFDTOTAL-ZZHD202301042.htm
|
| [9] |
XU M, MA C, HAN X J, et al. Influence of different optimization algorithms on prediction accuracy of photovoltaic output power based on bp neural network[C]//41st Chinese Control Conference (CCC). IEEE, 2022: 7275-7278. DOI: 10.23919/CCC55666.2022.9902165
|
| [10] |
臧运瑞. 基于特征选择和深度学习的红外测温方法[D]. 秦皇岛: 燕山大学, 2022.
ZANG Y R. Infrared Temperature Measurement Method Based on Feature Selection and Deep learning[D]. Qinhuangdao: Yanshan University, 2022.
|
| [11] |
XUE L Z, ZENG X Y, JIN A Q. A novel deep-learning method with channel attention mechanism for underwater target recognition[J]. Sensors, 2022, 22(15): 5492-5492. DOI: 10.3390/s22155492
|
| [12] |
SUN M B, CHEN S B. Deep learning-based super-resolution reconstruction and algorithm acceleration of mars hyperspectral CRISM data[J]. Remote Sensing, 2022, 14(13): 3062-3062. DOI: 10.3390/rs14133062
|
| [13] |
王岚. 基于跳跃连接注意力网络的音乐分离[J]. 电声技术, 2022, 46(2): 29-32. https://www.cnki.com.cn/Article/CJFDTOTAL-DSJS202202007.htm
WANG L. Music separation based on jump connected attention network[J]. Electroacoustic Technology, 2022, 46(2): 29-32. https://www.cnki.com.cn/Article/CJFDTOTAL-DSJS202202007.htm
|
| [14] |
王浩, 苗丽. 黑体辐射源校准方法及亮度温度不确定度评定[J]. 品牌与标准化, 2022, 375(4): 34-36. https://www.cnki.com.cn/Article/CJFDTOTAL-QYBZ202204012.htm
WANG H, MIAO L. Blackbody radiation source calibration method and luminance temperature uncertainty evaluation[J]. Brand and Standardization, 2022, 375(4): 34-36. https://www.cnki.com.cn/Article/CJFDTOTAL-QYBZ202204012.htm
|
| [15] |
CHAO D, CHEN C L, TANG X O. Image super-resolution using deep convolutional networks[J]. CoRR, 2015, 38(2): 295-307
|
| [16] |
Jiwon K, Jung Kwon L, Kyoung Mu L. Accurate image super-resolution using very deep convolutional networks[J]. CoRR, 2016, 1646-1654. DOI: 10.1109/CVPR.2016.182.
|
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| |
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