计算机科学 ›› 2016, Vol. 43 ›› Issue (2): 64-67.doi: 10.11896/j.issn.1002-137X.2016.02.014
• 2015年中国计算机学会人工智能会议 • 上一篇 下一篇
王宪保,何文秀,王辛刚,姚明海,钱沄涛
WANG Xian-bao, HE Wen-xiu, WANG Xin-gang, YAO Ming-hai and QIAN Yun-tao
摘要： 目前医用胶囊生产过程中的缺陷检测主要由人工完成,费时费力,容易受主观因素的影响。提出一种基于堆叠降噪自动编码器的胶囊表面缺陷检测方法,该方法首先建立深度自动编码器网络,并根据缺陷样本进行降噪训练,获取网络的初始权值；然后通过BP算法进行微调,得到训练样本到无缺陷模板之间的映射关系；最后利用重构图像与缺陷图像之间的对比关系,实现测试样本的缺陷检测。实验表明,堆叠降噪自动编码器较好地建立了上述映射关系,能快速、准确地进行缺陷检测,对噪声具有很强的鲁棒性和稳定性。
| [1] Hinton G E,Salakhutdinov R R.Reducing the dimensionality of data with neural networks[J].Science,2006,3(28):504-507 [2] Hinton G E,Osindero S,Teh Y-W.A fast learning algorithm for deep belief nets[J].Nueral Computation,2006,8(7):1527-1554 [3] Bengio Y.Learning deep architectures for AI[J].Foundations and Trends in Machine Learning,2009,2(1):1-127 [4] Dong Yu,Li Deng.Deep learning and its applications to signal and information processing[J].IEEE Signal Processing M agazine,2011,28(1):145-154 [5] Karnowski T P,Arel I,et al.Deep spatiotemporal feature lear-ning with application to image classification[C]∥Proceedings of the Ninth International Conference on Machine Learning and Applications.Washington,USA,2010:883-888 [6] Schulz H,Behnke S.Deep learning:layer-wise learning of fea-ture hierarchies[J].Künstl Intell,2012,26:357-363 [7] Vincent P,Larochelle H,Lajoie I,et al.Stacked denoising autoencoders:learning useful representations in a deep network with a local denoising criterion [J].Journal of Machine Learning Research,2010,11(12):3371-3408 [8] Kang Y,Lee K T,Eun J,et al.Stacked Denoising Autoencoders for Face Pose Normalization [C]∥20th International Confe-rence on Neural Information Processing.Berlin:Springer Press,2013:241-248 [9] Zhang Ying,Liu Rui,Zhang Sai-zheng,et al.Occlusion-Robust Face Recognition Using Iterative Stacked Denoising Autoenco-der[C]∥20th International Conference on Neural Information Processing.Berlin:Springer Press,2013:352-359 [10] Sang Ruo-xin,Jin Pei-quan,Wan Shou-hong.Discriminative Feature Learning for Action Recognition Using a Stacked Denoising Autoencoder[M]∥Intelligent Data Analysis and Its Applications, Volume1.2014:521-531 [11] Agostinelli F,Anderson M R,et al.Adaptive multi-column deep neural networks with application to robust image denoising[C]∥Proceedings of the Advances in Neural Information Processing Systems 26 (NIPS’12).Cambridge:MIT Press,2013:1-9 [12] Wei Lin,Hu Rong-qiang.The Automatic Inspection System ofCapsule Product Based on BP Neural Network[J].Application Research of Computers,2002,9(4):52-53(in Chinese)韦琳,胡荣强.基于BP网络的胶囊药片自动检测系统[J].计算机应用研究,2002,9(4):52-53 [13] Zuo Qi,Shi Zhong-ke.General design for capsule integrality detection system based on machine vision[J].Journal of Xi’an Jiaotong University,2002,36(12):1262-1265(in Chinese) 左奇,史忠科.基于机器视觉的胶囊完整性检测系统研究[J].西安交通大学学报,2002,36(12):1262-1265 [14] Feng Shan-shan,Chen Shu-yue.Research on identification me-thod for capsule-grain of real and false medicine based on image analyzing[J].Transducer and Micro system Technologies,2008,27(8):54-56(in Chinese) 冯姗姗,陈树越.基于图像分析的真假药胶囊颗料识别方法研究[J].传感器与微系统,2008,27(8):54-56 [15] Lai Da-hu,Huang Yan-wei.Inspection for defected capsulesbased on extreme learning machine[J].Journal of Fuzhou University (Natural Science Edition),2012,0(4):489-494(in Chinese) 赖大虎,黄宴委.基于极端学习机的胶囊缺陷检测[J].福州大学学报(自然科学版),2012,0(4):489-494 |
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