Citation: | DING Xiwen, CHENG Hongchang, YUAN Yuan, ZHANG Ruoyu, YANG Shuning, YANG Ye, DANG Xiaogang. Research Status of Local Defect Detection Technology of Ultraviolet Image Intensifier Field of View[J]. Infrared Technology , 2024, 46(2): 129-137. |
[1] |
石峰, 程宏昌, 闫磊, 等. 紫外探测技术[M]. 北京: 国防工业出版社, 2017.
SHI Feng, CHENG Hongchang, YAN Lei, et al. Ultraviolet Detection Technology[M]. Beijing: National Defense Industry Press, 2017.
|
[2] |
林祖伦, 王小菊. 光电成像导论[M]. 北京: 国防工业出版社, 2016.
LIN Zulun, WANG Xiaoju. Introduction to Photoelectric Imaging[M]. Beijing: National Defense Industry Press, 2016.
|
[3] |
汪贵华. 光电子器件[M]. 3版: 北京: 国防工业出版社, 2020.
WANG Guihua. Optoelectronic Devices[M]. 3rd edition: Beijing: National Defense Industry Press, 2020.
|
[4] |
许正光, 王霞, 王吉晖, 等. 像增强器视场缺陷检测方法研究[J]. 应用光学, 2005(3): 12-15. DOI: 10.3969/j.issn.1002-2082.2005.03.004
XU Zhengguang, WANG Xia, WANG Jihui, et al. Research of an approach to detect field defects of image intensifier[J]. Application Optics, 2005(3): 12-15. DOI: 10.3969/j.issn.1002-2082.2005.03.004
|
[5] |
王吉晖, 金伟其, 王霞, 等. 基于数学形态学的像增强器缺陷的图像检测方法[J]. 光学技术, 2005(3): 463-464, 467.
WANG Jihui, JIN Weiqi, WANG Xia, et al. Flaw inspection method for image tube based on image processing[J]. Optical Technology, 2005(3): 463-464, 467.
|
[6] |
赵清波. 宽光谱像增强器辐射增益和视场缺陷测试技术研究[D]. 南京: 南京理工大学, 2008.
ZHAO Qingbo. Research on Radiation Gain and Field Defect Test Technology of Wide Spectrum Image Intensifier[D]. Nanjing: Nanjing University of Science and Technology, 2008.
|
[7] |
FU Rongguo, WEI Yifang, YANG Qi, et al. The analysis of the defects of the view field of the UV image intensifier[C]//Sensors and Systems for Space Applications X of SPIE, 2017, 10196: 19-26.
|
[8] |
杨琦. 紫外像增强器视场缺陷检测技术研究[D]. 南京: 南京理工大学, 2011.
YANG Qi. Research on Defect Detection Technology of Ultraviolet Image Intensifier[D]. Nanjing: Nanjing University of Science and Technology, 2011.
|
[9] |
ZHOU B, LIU B, WU D. Research on testing field flaws of image intensifier based on spatio-temporal SNR[C]//5th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronic Materials and Devices for Detector, Imager, Display, and Energy Conversion Technology of SPIE, 2010, 7658: 691-695.
|
[10] |
孙文政. 基于深度学习和机器视觉的手机屏幕瑕疵检测方法研究[D]. 济南: 山东大学, 2019.
SUN Wenzheng. Research on Mobile Phone Screen Defect Detection Method Based on Deep Learning and Machine Vision[D]. Jinan: Shandong University, 2019.
|
[11] |
汤勃, 孔建益, 伍世虔. 机器视觉表面缺陷检测综述[J]. 中国图象图形学报, 2017, 22(12): 1640-1663.
TANG Bo, KONG Jianyi, WU Shiqian. Review of machine vision surface defect detection[J]. Chinese Journal of Image and Graphics, 2017, 22(12): 1640-1663.
|
[12] |
张涛, 刘玉婷, 杨亚宁, 等. 基于机器视觉的表面缺陷检测研究综述[J]. 科学技术与工程, 2020, 20(35): 14366-14376. DOI: 10.3969/j.issn.1671-1815.2020.35.004
ZHANG Tao, LIU Yuting, YANG Yaning, et al. Review of surface defect detection based on machine vision[J]. Science, Technology and Engineering, 2020, 20(35): 14366-14376. DOI: 10.3969/j.issn.1671-1815.2020.35.004
|
[13] |
KE Wang, WANG Huiqin, YUE Shu, et al. Banknote image defect recognition method based on convolution neural network[J]. International Journal of Security and Its Applications, 2016, 10(6): 269-280. DOI: 10.14257/ijsia.2016.10.6.26
|
[14] |
顾佳晨, 高雷, 刘路硌. 基于深度学习的目标检测算法在冷轧表面缺陷检测中的应用[J]. 冶金自动化, 2019, 43(6): 19-22.
GU Jiachen, GAO Lei, LIU Luke. Application of object detection algorithm based on deep learning for inspection of surface defect of cold rolled strips[J]. Metallurgical Automation, 2019, 43(6): 19-22.
|
[15] |
景军锋, 刘娆. 基于卷积神经网络的织物表面缺陷分类方法[J]. 测控技术, 2018, 37(9): 20-25.
JING Junfeng, LIU Rao. Classification method of fabric surface defects based on convolution neural network[J]. Measurement and Control Technology, 2018, 37(9): 20-25.
|
[16] |
Deitsch S, Christlein V, Berger S, et al. Automatic classification of defective photovoltaic module cells in electroluminescence images[J]. Solar Energy, 2019, 185: 455-468. DOI: 10.1016/j.solener.2019.02.067
|
[17] |
王森, 伍星, 张印辉, 等. 基于深度学习的全卷积网络图像裂纹检测[J]. 计算机辅助设计与图形学学报, 2018, 30(5): 859-867.
WANG Sen, WU Xing, ZHANG Yinhui, et al. Image crack detection with fully convolutional network based on deep learning[J]. Journal of Computer Aided Design and Graphics, 2018, 30(5): 859-867.
|
[18] |
DUNG C V. Autonomous concrete crack detection using deep fully convolutional neural network[J]. Automation in Construction, 2019, 99: 52-58. DOI: 10.1016/j.autcon.2018.11.028
|
[19] |
LIU Y, YANG Y, WANG C, et al. Research on surface defect detection based on semantic segmentation[C]//Advanced Science and Industry Research Center Proceedings of 2019 International Conference on Artificial Intelligence, Control and Automation Engineering(AICAE 2019), 2019: 416-420.
|
[20] |
DONG Y, WANG J, WANG Z, et al. A deep-learning-based multiple defect detection method for tunnel lining damages[J]. IEEE Access, 2019, 7: 182643-182657. DOI: 10.1109/ACCESS.2019.2931074
|
[21] |
TIAN H, LI F. Autoencoder-based fabric defect detection with cross-patch similarity[C]//16th International Conference on Machine Vision Applications (MVA) of IEEE, 2019: 1-6.
|
[22] |
WEI Y H, NI Y Q. Variational autoencoder-based approach for rail defect identification[C]//12th International Workshop on Structural Health Monitoring: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT), 2019: 2818-2824.
|
[23] |
DI H, KE X, PENG Z, et al. Surface defect classification of steels with a new semi-supervised learning method[J]. Optics and Lasers in Engineering, 2019, 117: 40-48. DOI: 10.1016/j.optlaseng.2019.01.011
|
[24] |
黄英来, 艾昕. 改进残差网络在玉米叶片病害图像的分类研究[J]. 计算机工程与应用, 2021, 57(23): 7.
HUANG Yinglai, AI Xin. Research on classification of corn leaf disease image by improved residual network[J]. Computer Engineering and Application, 2021, 57(23): 7.
|
[25] |
孙鹏翔, 毕利, 王俊杰. 基于改进深度残差网络的光伏板积灰程度识别[J]. 计算机应用, 2022, 42(12): 3733-3739.
SUN Pengxiang, BI Li, WANG Junjie. Dust accumulation degree recognition of photovoltaic panel based on improved deep residual network[J]. Computer Application, 2022, 42(12): 3733-3739.
|
[26] |
李馥颖, 杨大为, 黄海. 基于改进深度置信网络的木板表面缺陷检测模型[J]. 南京理工大学学报, 2022, 46(6): 728-734.
LI Fuying, YANG Dawei, HUANG Hai. Improved deep belief network based detection model for wood surface defects[J]. Journal of Nanjing University of Science and Technology, 2022, 46(6): 728-734.
|
[27] |
黄振宁, 赵永贵, 许志亮, 等. 基于判别式深度置信网络的智能电缆隧道缺陷检测技术研究[J]. 电子设计工程, 2022, 30(20): 103-107.
HUANG Zhenning, ZHAO Yonggui, XU Zhiliang, et al. Fault detection technology for smart cable tunnel based on discriminant deep belief network[J]. Electronic Design Engineering, 2022, 30(20): 103-107.
|
[28] |
李文俊, 陈斌, 李建明, 等. 基于深度神经网络的表面划痕识别方法[J]. 计算机应用, 2019, 39(7): 2103-2108.
LI Wenjun, CHEN Bin, LI Jianming, et al. Surface scratch recognition method based on deep neural network[J]. Computer Application, 2019, 39(7): 2103-2108.
|
[29] |
LEI J, GAO X, FENG Z, et al. Scale insensitive and focus driven mobile screen defect detection in industry[J]. Neurocomputing, 2018, 294: 72-81. DOI: 10.1016/j.neucom.2018.03.013
|
[1] | CHEN Xu, WU Wei, PENG Dongliang, GU Yu. Infrared-PV: an Infrared Target Detection Dataset for Surveillance Application[J]. Infrared Technology , 2023, 45(12): 1304-1313. |
[2] | LEI Yongchang, LI Jianlin, DONG Wei, ZHOU Jiading, HOU Likun, QIAN Kunlun. Redundant Object Damage and Prevention Method for Infrared Detectors[J]. Infrared Technology , 2023, 45(7): 790-797. |
[3] | ZHANG Lu, ZHANG Lei, FU Zhikai, TIAN Ya. Low Temperature Evaluation Method of Infrared Detector Integrated with Optical System[J]. Infrared Technology , 2021, 43(12): 1188-1192. |
[4] | ZHANG Kunjie. Research Progress and Trends of High Operating Temperature Infrared Detectors[J]. Infrared Technology , 2021, 43(8): 766-772. |
[5] | ZHU Shuangshuang, ZOU Peng, LU Meina, ZHANG Aiwen, LIU Zhenhai, QIU Zhenwei, HONG Jin. Temperature Control System Design of Infrared Detector Based on Bang-Bang and PID Control[J]. Infrared Technology , 2017, 39(11): 990-995. |
[6] | High Performance InP/InGaAs Wide Spectrum Infrared Detectors[J]. Infrared Technology , 2016, 38(1): 1-5. |
[7] | YONG Chao-Liang, DUAN Dong, XU Chun, CHEN Fan-Sheng. The Study on the Dark Current of the Infrared Detector Measuring Method[J]. Infrared Technology , 2012, 34(4): 196-199. DOI: 10.3969/j.issn.1001-8891.2012.04.003 |
[8] | Performance HgCdTe Infrared Detector at Different Temperatures[J]. Infrared Technology , 2012, 34(1): 1-3,15. DOI: 10.3969/j.issn.1001-8891.2012.01.001 |
[9] | ZHANG Tong, CHEN Xiao-wen, LIU Yin-nian, WANG Jian-yu. Design and Implement of Temperature Controlling System of Spaceborne Infrared Detector[J]. Infrared Technology , 2005, 27(2): 167-170. DOI: 10.3969/j.issn.1001-8891.2005.02.017 |
[10] | An Inquiry into the Performances of the Far Infrared Magnetic Fibers[J]. Infrared Technology , 2002, 24(6): 86-89. DOI: 10.3969/j.issn.1001-8891.2002.06.020 |