[1]马岭,鲁越,蒋慧琴,等.基于小样本学习的LCD产品缺陷自动检测方法[J].智能系统学报,2020,15(3):560-567.[doi:10.11992/tis.201904020]
 MA Ling,LU Yue,JIANG Huiqin,et al.An automatic small sample learning-based detection method for LCD product defects[J].CAAI Transactions on Intelligent Systems,2020,15(3):560-567.[doi:10.11992/tis.201904020]
点击复制

基于小样本学习的LCD产品缺陷自动检测方法(/HTML)
分享到:

《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第15卷
期数:
2020年3期
页码:
560-567
栏目:
学术论文—机器学习
出版日期:
2020-09-05

文章信息/Info

Title:
An automatic small sample learning-based detection method for LCD product defects
作者:
马岭1 鲁越1 蒋慧琴1 刘玉敏2
1. 郑州大学 信息工程学院,河南 郑州 450001;
2. 郑州大学 商学院,河南 郑州 450001
Author(s):
MA Ling1 LU Yue1 JIANG Huiqin1 LIU Yumin2
1. School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China;
2. Business School, Zhengzhou University, Zhengzhou 450001, China
关键词:
缺陷诊断目标分类深度学习小样本学习卷积神经网络迁移学习深度卷积生成对抗网络继续学习
Keywords:
defect diagnosticstarget classificationdeep learningsmall sample learningconvolution neural networktransfer learningDCGANcontinue learning
分类号:
TP391
DOI:
10.11992/tis.201904020
摘要:
针对高分辨率液晶显示器产品(liquid crystal display, LCD)质量在线检测需求,基于深度学习提出一种LCD缺陷自动检测方法。通过设计自适应浅层特征提取层,并引入稀疏卷积结构,多维度、多尺度的提取深层特征,采用迁移学习和深度卷积生成对抗生网络扩充数据强化训练,构建基于小样本学习的LCD表面缺陷检测模型。其特征在于,采用设计的自动分割与定位预处理软件将高分辨率图像划分成适于卷积神经网络学习的图像子块,并根据模型对图像子块的判定类别和定位坐标,同时获取多类型缺陷检测结果。实验结果表明,本文模型可以有效提高检出率,并减少漏检率。
Abstract:
Aiming at the demands of high-resolution liquid crystal display (LCD) product quality online inspection, we propose an automatic deep learning-based detection method for LCD defects. An LCD surface defect detection model is constructed based on small sample learning by designing an adaptive shallow feature extraction layer and introducing a sparse convolution structure to extract the multi-dimensional and multi-scale deep features. Furthermore, the model is trained and enhanced using transfer learning and a deep convolutional generative adversarial network (DCGAN) for data expansion, and the LCD surface defect detection model is built based on small sample learning. The original high-resolution image is segmented into sub-images suitable for convolutional neural network (CNN) learning by designing automatic segmentation and location pretreatment software. The detection results of multi-type defects according to the category and location coordinates of the classification model’s output are obtained. The experimental results show that the model can effectively improve the detection rate and reduce the missed detection rate.

参考文献/References:

[1] LU Rongsheng, SHI Yanqiong, LI Qi, et al. AOI techniques for surface defect inspection[J]. Applied mechanics and materials, 2010, 36: 297-302.
[2] LIU Y H, LIU Yanchen, CHEN Y Z. High-speed inline defect detection for TFT-LCD array process using a novel support vector data description[J]. Expert systems with applications, 2011, 38(5): 6222-6231.
[3] CEN Yigang, ZHAO Ruizhen, CEN Lihui, et al. Defect inspection for TFT-LCD images based on the low-rank matrix reconstruction[J]. Neurocomputing, 2015, 149: 1206-1215.
[4] 张腾达, 卢荣胜, 张书真. 基于二维DFT的TFT-LCD平板表面缺陷检测[J]. 光电工程, 2016, 43(3): 7-15
ZHANG Tengda, LU Rongsheng, ZHANG Shuzhen. Surface defect inspection of TFT-LCD panels based on 2D DFT[J]. Opto-electronic engineering, 2016, 43(3): 7-15
[5] MA Ling, LIU Wei, LIU Yumin, et al. An automatic detection algorithm for surface defects in TFT-LCD[C]//Proceedings of 2013 Second IAPR Asian Conference on Pattern Recognition. Naha, Japan, 2013: 847-851.
[6] 马岭, 蒋慧琴, 刘玉敏. 基于局部特征的驾驶证自动识别系统[J]. 郑州大学学报(工学版), 2017, 38(5): 13-17, 22
MA Ling, JIANG Huiqin, LIU Yumin. Automatic recognition system of driver’s license based on local features[J]. Journal of Zhengzhou University (engineering science edition), 2017, 38(5): 13-17, 22
[7] L?NGKVIST M, KARLSSON L, LOUTFI A. A review of unsupervised feature learning and deep learning for time-series modeling[J]. Pattern recognition letters, 2014, 42: 11-24.
[8] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems. Siem Reap, Cambodia, 2012: 1097-1105.
[9] BENGIO Y, COURVILLE A, VINCENT P. Representation learning: a review and new perspectives[J]. IEEE transactions on pattern analysis and machine intelligence, 2013, 35(8): 1798-1828.
[10] DENG Jia, BERG A, SATHEESH S, et al. Large scale visual recognition challenge[EB/OL]. [2013-11-14]. http://image-net.org/challenges/LSVRC/2013/.
[11] EVERINGHAM M, ALI ESLAMI S M, VAN GOOL L, et al. The PASCAL visual object classes challenge: a retrospective[J]. International journal of computer vision, 2015, 111(1): 98-136.
[12] TAJBAKHSH N, SUZUKI K. Comparing two classes of end-to-end machine-learning models in lung nodule detection and classification: MTANNs vs. CNNs[J]. Pattern recognition, 2017, 63: 476-486.
[13] 郑胤, 陈权崎, 章毓晋. 深度学习及其在目标和行为识别中的新进展[J]. 中国图象图形学报, 2014, 19(2): 175-184
ZHENG Yin, CHEN Quanqi, ZHANG Yujin. Deep learning and its new progress in object and behavior recognition[J]. Journal of image and graphics, 2014, 19(2): 175-184
[14] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90.
[15] ZHONG Zhuoyao, JIN Lianwen, XIE Zecheng. High performance offline handwritten chinese character recognition using googlenet and directional feature maps[C]//Proceedings of 2015 13th International Conference on Document Analysis and Recognition. Tunis, Tunisia, 2015: 846-850.
[16] SZEGEDY C, IOFFE S, VANHOUCKE V, et al. Inception-V4, inception-ResNet and the impact of residual connections on learning[C]//Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. San Francisco, USA, 2017.
[17] Lin M, Chen Q, Yan S. Network In Network[C]//Proceedings of the 2th International Conference on Learning Representations. Banff, Canada, 2016.
[18] SZEGEDY C, LIU Wei, JIA Yangqing, et al. Going deeper with convolutions[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA, 2015: 1-9.
[19] Radford A, Metz L, Chintala S. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks[C]//Proceedings of the 4th International Conference on Learning Representations. San Juan, Puerto Rico, 2016.
[20] GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems. Montreal, Canada, 2014: 2672-2680.

备注/Memo

备注/Memo:
收稿日期:2019-04-09。
基金项目:国家自然科学基金—河南联合基金重点项目(U1604262)
作者简介:马岭,教授,博士,主要研究方向为深度学习和机器视觉。主持NSFC-河南联合基金重点项目1项,获河南省科技进步一等奖1项,获发明专利授权5项。发表学术论文30余篇,出版专著1部;鲁越,硕士研究生,主要研究方向为深度学习和机器视觉;蒋慧琴,教授,博士,主要研究方向为深度学习和医疗人工智能。主持和参与完成国家自然科学基金面上项目2项、省部级项目4项,获发明专利授权3项。发表学术论文50余篇
通讯作者:马岭.E-mail:ielma@zzu.edu.cn
更新日期/Last Update: 1900-01-01