Few-Shot Metric Transfer Learning Network for Surface Defect Detection
HUANG Jian1, ZHENG Chunhou1, ZHANG Jun2, WANG Bing3, CHEN Peng4
1. School of Computer Science and Technology, Anhui University, Hefei 230601; 2. School of Electrical Engineering and Automation, Anhui University, Hefei 230601; 3. School of Electrical and Information Engineering, Anhui University of Technology, Maanshan 243032; 4. School of Internet, Anhui University, Hefei 230601
Abstract Metric learning method of few-shot learning is introduced into the field of defect detection, and a few-shot learning method based on transfer metric learning is proposed to meet the requirement of deep learning method for a large number of learning samples. In the first stage, the deep network is pre-trained on the large datasets which are open or easy to be obtained. In the second stage, the relevant knowledge learned by the network is transferred to the field of surface defect detection through the metric learning module.Experiments show the feasibility of few-shot learning in defect detection.
Fund:National Natural Science Foundation of China(No.62072002,U19A2064,61872004)
Corresponding Authors:
CHEN Peng, Ph.D., professor. His research interests include computer vision and data analysis.
About author:: HUANG Jian, master student. His research interests include computer vision and image processing.ZHENG Chunhou, Ph.D., professor. His research interests include data analysis and bioinformatics.ZHANG Jun, Ph.D., professor. His research interests include pattern recognition, intelligent information processing and bioinformatics.WANG Bing, Ph.D., professor. His research interests include intelligent information processing, computer vision and image processing.
[1] 刘 泽,王 嵬,王 平.钢轨表面缺陷检测机器视觉系统的设计.电子测量与仪器学报,2010,24(11):1012-1017. (LIU Z,WANG W,WANG P.Design of Machine Vision System for Inspection of Rail Surface Defects.Journal of Electronic Mea-surement and Instrument,2010,24(11):1012-1017.) [2] 李景彬,邓向武,坎 杂,等.机器视觉在农产品表面缺损检测中的应用研究.农机化研究,2013,35(7):221-224. (LI J B,DENG X W,KAN Z,et al.Review of Machine Vision Research in Surface Defect Detection of Agricultural Products.Journal of Agricultural Mechanization Research,2013,35(7):221-224.) [3] 何存富,刘 溯,刘增华,等.小波降噪在钢绞线缺陷检测中的应用.机械工程学报,2008,44(7):118-122. (HE C F,LIU S,LIU Z H,et al.Application of Wavelet Denoise in Defect Inspection of Steel Strands.Chinese Journal of Mechanical Engineering,2008,44(7):118-122.) [4] KRIZHEVSKY A,SUTSKEVER I,HINTON G E.ImageNet Classification with Deep Convolutional Neural Networks//Proc of the 25th International Conference on Neural Information Processing Systems.Cambridge,USA:The MIT Press,2012,I:1097-1105. [5] SIMONYAN K,ZISSERMAN A.Very Deep Convolutional Networks for Large-Scale Image Recognition[C/OL].[2021-01-12].https://arxiv.org/pdf/1409.1556.pdf. [6] HE K M,ZHANG X Y,REN S Q,et al.Deep Residual Learning for Image Recognition//Proc of the IEEE Conference on Computer Vision and Pattern Recognition.Washington,USA:IEEE,2016:770-778. [7] MASCI J,MEIER U,CIRESAN D,et al.Steel Defect Classification with Max-Pooling Convolutional Neural Networks//Proc of the International Joint Conference on Neural Networks.Washington,USA:IEEE,2012.DOI:10.1109/IJCNN.2012.6252468. [8] 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.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.Pattern Recognition and Artificial Intelligence,2014,27(6):517-523.) [9] 刘 云,杨建滨,王传旭.基于卷积神经网络的苹果缺陷检测算法.电子测量技术,2017,40(3):108-112. (LIU Y,YANG J B,WANG C X.Apple Defect Detection Algorithm Based on Convolution Neural Network.Electronic Measurement Technology,2017,40(3):108-112.) [10] 王永利,曹江涛,姬晓飞.基于卷积神经网络的PCB缺陷检测与识别算法.电子测量与仪器学报,2019,33(8):78-84. (WANG Y L,CAO J T,JI X F.PCB Defect Detection and Re-cognition Algorithm Based on Convolutional Neural Network.Journal of Electronic Measurement and Instrument,2019,33(8):78-84.) [11] 金侠挺,王耀南,张 辉,等.基于贝叶斯CNN和注意力网络的钢轨表面缺陷检测系统.自动化学报,2019,45(12):2312-2327. (JIN X T,WANG Y N,ZHANG H,et al. DeepRail:Automatic Visual Detection System for Railway Surface Defect Using Bayesian CNN and Attention Network.Acta Automatica Sinica,2019,45(12):2312-2327.) [12] CHEN L C,ZHU Y K,PAPANDREOU G,et al. Encoder-Deco-der with Atrous Separable Convolution for Semantic Image Segmentation//Proc of the European Conference on Computer Vision.Berlin, Germany:Springer,2018:833-851. [13] CHOLLET F.Xception:Deep Learning with Depthwise Separable Convolutions//Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington,USA:IEEE,2017:1251-1258. [14] ZHANG J B,SU H,ZOU W,et al.CADN:A Weakly Supervised Learning-Based Category-Aware Object Detection Network for Surface Defect Detection.Pattern Recognition,2021,109.DOI:10.1016/j.patcog.2020.107571. [15] REN S Q,HE K M,GIRSHICK R,et al.Faster R-CNN:Towards Real-Time Object Detection with Region Proposal Networks//Proc of the 28th International Conference on Neural Information Proce-ssing Systems.Cambridge,USA:The MIT Press,2015,I:91-99. [16] LIN G S,MILAN A,SHEN C H,et al. RefineNet:Multi-path Refinement Networks for High-Resolution Semantic Segmentation//Proc of the IEEE Conference on Computer Vision and Pattern Recognition.Washington,USA:IEEE,2017:5168-5177. [17] VINYALS O,BLUNDELL C,LILLIERAP T,et al.Matching Networks for One Shot Learning//Proc of the 30th International Conference on Neural Information Processing Systems.Cambridge,USA:The MIT Press,2016:3637-3645. [18] SNELL J,SWERSKY K,ZEMEL R S.Prototypical Networks for Few-Shot Learning//Proc of the 31st International Conference on Neural Information Processing Systems.Cambridge,USA:The MIT Press,2017:4080-4090. [19] LI W B,WANG L,XU J L,et al.Revisiting Local Descriptor Based Image-to-Class Measure for Few-Shot Learning//Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington,USA:IEEE,2019:7260-7268. [20] SUNG F,YANG Y X,ZHANG L,et al. Learning to Compare:Relation Network for Few-Shot Learning//Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington,USA:IEEE,2018:1199-1208. [21] DENG J,DONG W,SOCHER R,et al. ImageNet:A Large-Scale Hierarchical Image Database//Proc of the IEEE Conference on Computer Vision and Pattern Recognition.Washington,USA:IEEE,2009:248-255. [22] SONG K C,YAN Y H.A Noise Robust Method Based on Completed Local Binary Patterns for Hot-Rolled Steel Strip Surface Defects.Applied Surface Science,2013,285(B):858-864. [23] ZAGORUYKO S,KOMODAKIS N.Wide Residual Networks[C/OL].[2021-01-12].https://arxiv.org/pdf/1605.07146v3.pdf. [24] WAH C,BRANSON S,WELINDER P,et al.The Caltech-UCSD Birds-200-2011 Dataset.Computation and Neural Systems Technical Report,CNS-TR-2011-001.Los Angeles,USA:California In-stitute of Technology,2011.
2021, Vol. 34 
