基于信号突变点校正的太阳能电池片缺陷检测方法 下载: 1118次
范程华, 王群京, 曹欣远, 陈兵兵, 齐琦. 基于信号突变点校正的太阳能电池片缺陷检测方法[J]. 激光与光电子学进展, 2020, 57(6): 061101.
Chenghua Fan, Qunjing Wang, Xinyuan Cao, Bingbing Chen, Qi Qi. Defect Detection Method for Solar Cell Based on Signal Catastrophe-Points Correction[J]. Laser & Optoelectronics Progress, 2020, 57(6): 061101.
[1] 卢荣胜, 吴昂, 张腾达, 视觉, 等. 检测技术及其在缺陷检测中的应用综述[J]. 光学学报, 2018, 38(8): 0815002.
[2] 闵永智, 岳彪, 马宏锋, 等. 基于图像灰度梯度特征的钢轨表面缺陷检测[J]. 仪器仪表学报, 2018, 39(4): 220-229.
Min Y Z, Yue B, Ma H F, et al. Rail surface defects detection based on gray scale gradient characteristics of image[J]. Chinese Journal of Scientific Instrument, 2018, 39(4): 220-229.
[3] Khanmohammadi S, Adibeig N, Shanehbandy S. An improved overlapping k-means clustering method for medical applications[J]. Expert Systems with Applications, 2017, 67: 12-18.
[4] 叶松, 熊伟, 王新强, 等. 基于频域分析的空间外差干涉图校正方法研究[J]. 光学学报, 2013, 33(5): 0530001.
[5] 姚明海, 李洁, 王宪保. 基于RPCA的太阳能电池片表面缺陷检测[J]. 计算机学报, 2013, 36(9): 1943-1952.
Yao M H, Li J, Wang X B. Solar cells surface defects detection using RPCA method[J]. Chinese Journal of Computers, 2013, 36(9): 1943-1952.
[6] 王宪保, 李洁, 姚明海, 等. 基于深度学习的太阳能电池片表面缺陷检测方法[J]. 模式识别与人工智能, 2014, 27(6): 517-523.
Wang X B, Li J, Yao M H, et al. Solar cells surface defects detection based on deep learning[J]. Pattern Recognition and Artificial Intelligence, 2014, 27(6): 517-523.
[7] Cao X Y, Chen M S, Wu X L, et al. Dual compressed sensing method for solving electromagnetic scattering problems by method of moments[J]. IEEE Antennas and Wireless Propagation Letters, 2018, 17(2): 267-270.
[8] Cao X Y, Chen M S, Wu X L, et al. Bilateral sparse transform for fast solving EM scattering problems by compressed sensing[J]. IET Microwaves, Antennas & Propagation, 2017, 11(14): 2049-2053.
[9] 李志斌, 黄启韬, 刘畅, 等. 基于小波变换的重叠光纤布拉格光栅信号峰值定位[J]. 激光与光电子学进展, 2017, 54(10): 100604.
[10] 陈平, 王佳昌, 吴兴研. 能量重心法的改进FFT算法分析及应用研究[J]. 机械科学与技术, 2018, 37(12): 1883-1889.
Chen P, Wang J C, Wu X Y. Analysis and application of an improved FFT algorithm for energy centrobaric correction method[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(12): 1883-1889.
[11] Boyer-Provera E, Rossi A, Oriol L, et al. Wavelet-based decomposition of high resolution surface plasmon microscopy V(Z) curves at visible and near infrared wavelengths[J]. Optics Express, 2013, 21(6): 7456-7477.
[12] Zeng J, Cheung G, Ortega A. Bipartite approximation for graph wavelet signal decomposition[J]. IEEE Transactions on Signal Processing, 2017, 65(20): 5466-5480.
[13] Hachicha W, Kaaniche M, Beghdadi A, et al. No-reference stereo image quality assessment based on joint wavelet decomposition and statistical models[J]. Signal Processing: Image Communication, 2017, 54: 107-117.
[14] Singh K R, Chaudhury S. Efficient technique for rice grain classification using back-propagation neural network and wavelet decomposition[J]. IET Computer Vision, 2016, 10(8): 780-787.
[15] 于啸, 洪光烈, 凌元, 等. 能量重心校正法在提高激光多普勒测速精度中的应用[J]. 激光与光电子学进展, 2012, 49(9): 091201.
[16] Kang D, Liqi J. Energy centrobaric correction method for discrete spectrum[J]. Journal of Vibration Engineering, 2001, 14(3): 354-358.
[17] 侯盼卫, 杨录, 岳文豹. 应用FFT的高精度FMCW雷达频率测量算法[J]. 自动化仪表, 2014, 35(3): 17-19.
Hou P W, Yang L, Yue W B. FFT-based high precision Frequency measurement algorithm for FM CW radar[J]. Process Automation Instrumentation, 2014, 35(3): 17-19.
[18] 赵慧阳. 基于机器视觉的太阳能电池片表面缺陷检测的研究[D]. 秦皇岛: 燕山大学, 2011: 43- 63.
Zhao HY. Research on surface defect inspection of solar cell based on machine vision[D]. Qinhuangdao: Yanshan University, 2011: 43- 63.
[19] 钱晓亮, 张鹤庆, 张焕龙, 等. 基于视觉显著性的太阳能电池片表面缺陷检测[J]. 仪器仪表学报, 2017, 38(7): 1570-1578.
Qian X L, Zhang H Q, Zhang H L, et al. Solar cell surface defect detection based on visual saliency[J]. Chinese Journal of Scientific Instrument, 2017, 38(7): 1570-1578.
范程华, 王群京, 曹欣远, 陈兵兵, 齐琦. 基于信号突变点校正的太阳能电池片缺陷检测方法[J]. 激光与光电子学进展, 2020, 57(6): 061101. Chenghua Fan, Qunjing Wang, Xinyuan Cao, Bingbing Chen, Qi Qi. Defect Detection Method for Solar Cell Based on Signal Catastrophe-Points Correction[J]. Laser & Optoelectronics Progress, 2020, 57(6): 061101.