Computer Science ›› 2024, Vol. 51 ›› Issue (6A): 230500180-8.doi: 10.11896/jsjkx.230500180

• Image Processing & Multimedia Technolog • Previous Articles     Next Articles

Classification of Multiscale Steel Microstructure Images Based on Incremental Learning

ZENG Peiyi   

  1. School of Computer Science and Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China
  • Published:2024-06-06
  • About author:ZENG Peiyi,born in 2002,undergra-duate.His main research interests include artificial intelligence and new methods of deep learning and their applications.
  • Supported by:
    National Natural Science Foundation of China(51774219).

Abstract: The mechanical properties of steels are closely related to their microstructures,so it is important to identify the microstructures of steels.The magnification of steel micrograph varies greatly,and the morphology of the same microstructure at different magnifications is also different,so the classification of the continuously expanded multi-scale steel microstructure dataset is difficult.In this paper,VGG16 and self-organizing incremental neural network(SOINN) are combined to build a classification model for multiscale steel microstructure dataset based on incremental learning.In addition,the cross entropy loss based on center distance(CELCD) and cross train strategy are proposed.Combining with cross train,CELCD and anchor loss are utilized to solve the problem of “cata-strophic forgetting” and realize the incremental learning and efficient classification for steel micrographs.The classification accuracy and “forgotten degree” of the model are compared.Experimental results show that after incremental learning,the classification accuracy of the proposed method is only 14.02% lower than that before incremental learning,which reaches 80.49% on the old data and only 5.49% lower than the upper bound,which is superior to other incremental learning methods.

Key words: Steel microstructures, Incremental learning, Catastrophic forgetting, Multi-scale, Self-organizing

CLC Number: 

  • TP391.41
[1]AZIMI S M,BRITZ D,ENGSTLER M,et al.Advanced steel microstructural classification by deep learning methods[J].Scientific Reports,2018,8(1):2128-2128.
[2]LI W G,YANG W,ZHAO Y T,et al.A new method to predict mechanical properties for micro-alloyed steels via industrial data and mechanism analysis[J].Journal of Iron and Steel Research International,2019,26(3):230-241.
[3]MAISURADZE M V,RYZHKOV M A,LEBEDEV D I.Microstructure of the Heat Treated Advanced Low Carbon Steel[J].Solid State Phenomena,2021,316:252-257.
[4]LI W G,SHEN J C,XIE L et al.Semi Supervised Learning of Metallographic Data Based on Self-organizing Incremental and Graph Convolution Neural Network[J].Journal of Electronics &Information Technology,2021,43:1-8.
[5]LOSING V,HAMMER B,WERSING H.Incremental on-line learning:A review and comparison of state of the art algorithms[J].Neurocomputing,2018,275:1261-1274.
[6]YU C,ZHOU Q,LI J,et al.Foundation Model Drives Weakly Incremental Learning for Semantic Segmentation[C]//Procee-dings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2023:23685-23694.
[7]ZHU F,ZHANG X Y,WANG C,et al.Prototype augmentation and self-supervision for incremental learning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2021:5871-5880.
[8]CHOWDHURY A,KAUTZ E,YENER B,et al.Image drivenmachine learning methods for microstructure recognition[J].Computational Materials Science,2016,123:176-187.
[9]LUBBERS N,LOOKMAN T,BARROS K.Inferring low-dimensional microstructure representations using convolutional neural networks[J].Physical Review E,2017,96(5):052111.
[10]DECOST B L,FRANCIS T,HOLM E A.Exploring the microstructure manifold:Image texture representations applied to ultrahigh carbon steel microstructures[J].Acta Materialia,2017,133:30-40.
[11]LI W G,SHEN J C,FAN L X et al.Automatic identification of microstructure of iron and steel material based on convolutional neural network[J].Journal of Iron and Steel Research,2020,32(1):33-43.
[12]MALLYA A,LAZEBNIK S.Packnet:Adding multiple tasks to a single network by iterative pruning[C]//Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake City,Utah:2018:7765-7773.
[13]FERNANDO C,BANARSE D,BLUNDELL C,et al.PathNet:Evolution channels gradient descent in super neural networks[J].arXiv:1701.08734,2017.
[14]RUSU A A,RABINOWITZ N C,DESJARDINS G,et al.Progressive neural networks[J].arXiv:1606.04671,2016.
[15]REBUFFI S A,KOLESNIKOV A,SPERL G,et al.ICaRL:Incremental classifier and representation learning[C]//Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition.Honolulu,Hawaii.2017:5533-5542.
[16]HINTON G,VINYALS O,DEAN J.Distilling the knowledge in a neural network[C]//Proceeding of the Conference and Workshop on Neural Information Processing Systems.Palais des Congrès de Montréal,Canada,2014.
[17]CHAUDHRY A,RANZATO M,ROHRBACH M,et al.Efficient lifelong learning with A-GEM[C]//Proceeding of the International Conference on Learning Representations.Vancouver,Canada,2018.
[18]CASTRO F M,MARÍN-JIMÉNEZ M J,GUIL N,et al.End-to-end incremental learning[C]//Proceeding of the European Conference on Computer Vision.Munich,Germany 2018:233-248.
[19]WU Y,CHEN Y,WANG L,et al.Large scale incrementallearning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Long Beach,California,2019:374-382.
[20]ALJUNDI R,LIN M,GOUJAUD B,et al.Gradient based sample selection for online continual learning[J].Advances in Neural Information Processing Systems,2019,32:11816-11825.
[21]WANG Z,SHEN Y.Incremental Learning for Multi-Interest Sequential Recommendation[C]//2023 IEEE 39th International Conference on Data Engineering(ICDE).IEEE,2023:1071-1083.
[22]LI Z,HOIEM D.Learning without forgetting[J].IEEE Tran-sactions on Pattern Analysis and Machine Intelligence,2017,40(12):2935-2947.
[23]KIRKPATRICK J,PASCANU R,RABINOWITZ N,et al.Overcoming catastrophic forgetting in neural networks[J].Proc. Natl. Acad. Sci. USA,2016,114(13):3521-3526.
[24]DHAR P,SINGH R V,PENG K C,et al.Learning withoutmemorizing[C]//Proceeding of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Long Beach,California,2019:5138-5146.
[25]HOU S,PAN X,LOY C C,et al.Learning a unified classifier incrementally via rebalancing[C]//Proceeding of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Long Beach,California,2019:831-839.
[26]ZHANG J,ZHANG J,GHOSHS,et al.Class-incremental lear-ning via deep model consolidation[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision.Snowmass Village,USA.2020:1131-1140.
[27]TAO X,HONG X,CHANG X,et al.Few-shot class-incremental learning[C]//Proceeding of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:12183-12192.
[28]KOHONEN T.Self-organized formation of topologically correct feature maps[J].Biological Cybernetics,1982,43(1):59-69.
[29]MARTINETZ T,SCHULTEN K.A “neural-gas” networklearns topologies[J/OL].Artificial Neural Networks,1991,397-402.https://www.ks.uiuc.edu/Publications/Papers/PDF/MART91B/MART91B.pdf.
[30]MARTINETZ T M,BERKOVICH S G,SCHULTEN K J.Neural Gas network for vector quantization and its application to time-series prediction[J].IEEE Transactions on Neural Networks,1993,4(4):558-569.
[31]MARTINETZ T M,SCHULTEN K J.Topology representing network[J].Neural Networks,1994,7(3):507-522.
[32]GRAY R M.Vector quantization[J].IEEE ASSP Magazine,1984,1(2):4-29.
[33]FRITZKE B.A growing neural gas network learns topologies[J].Advances in Neural Information Processing Systems,1995,7:625-632.
[34]FURAO S,HASEGAWAO.An incremental network for on-line unsupervised classification and topology learning[J].Neural Networks,2006,19(1):90-106.
[35]SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[J].arXiv:1409.1556,2014.
[36]WEN Y,ZHANG K,LI Z,et al.A discriminative feature learning approach for deep face recognition[C]//European Confe-rence on Computer Vision.Cham:Springer,2016:499-515.
[37]KINGMA D,BA J.Adam:A method for stochastic optimization[J].arXiv:1412.6980,2014.
[1] YANG Pengyue, WANG Feng, WEI Wei. ConvNeXt Feature Extraction Study for Image Data [J]. Computer Science, 2024, 51(6A): 230500196-7.
[2] LIU Xiaohu, CHEN Defu, LI Jun, ZHOU Xuwen, HU Shan, ZHOU Hao. Speaker Verification Network Based on Multi-scale Convolutional Encoder [J]. Computer Science, 2024, 51(6A): 230700083-6.
[3] LANG Lang, CHEN Xiaoqin, LIU Sha, ZHOU Qiang. Detection of Pitting Defects on the Surface of Ball Screw Drive Based on Improved Deeplabv3+ Algorithm [J]. Computer Science, 2024, 51(6A): 240200058-6.
[4] GAO Nan, ZHANG Lei, LIANG Ronghua, CHEN Peng, FU Zheng. Scene Text Detection Algorithm Based on Feature Enhancement [J]. Computer Science, 2024, 51(6): 256-263.
[5] BAI Xuefei, SHEN Wucheng, WANG Wenjian. Salient Object Detection Based on Feature Attention Purification [J]. Computer Science, 2024, 51(5): 125-133.
[6] HE Xiaohui, ZHOU Tao, LI Panle, CHANG Jing, LI Jiamian. Study on Building Extraction from Remote Sensing Image Based on Multi-scale Attention [J]. Computer Science, 2024, 51(5): 134-142.
[7] WU Xiaoqin, ZHOU Wenjun, ZUO Chenglin, WANG Yifan, PENG Bo. Salient Object Detection Method Based on Multi-scale Visual Perception Feature Fusion [J]. Computer Science, 2024, 51(5): 143-150.
[8] XU Hao, LI Fengrun, LU Lu. Metal Surface Defect Detection Method Based on Dual-stream YOLOv4 [J]. Computer Science, 2024, 51(4): 209-216.
[9] ZHANG Yang, XIA Ying. Object Detection Method with Multi-scale Feature Fusion for Remote Sensing Images [J]. Computer Science, 2024, 51(3): 165-173.
[10] WU Liuchen, ZHANG Hui, LIU Jiaxuan, ZHAO Chenyang. Defect Detection of Transmission Line Bolt Based on Region Attention Mechanism andMulti-scale Feature Fusion [J]. Computer Science, 2023, 50(6A): 220200096-7.
[11] YANG Ye, WU Weizhi, ZHANG Jiaru. Optimal Scale Selection and Rule Acquisition in Inconsistent Generalized Decision Multi-scale Ordered Information Systems [J]. Computer Science, 2023, 50(6): 131-141.
[12] HU Shaokai, HE Xiaohui, TIAN Zhihui. Land Use Multi-classification Method of High Resolution Remote Sensing Images Based on MLUM-Net [J]. Computer Science, 2023, 50(5): 161-169.
[13] WANG Lin, MENG Zuqiang, YANG Lina. Chinese Sentiment Analysis Based on CNN-BiLSTM Model of Multi-level and Multi-scale Feature Extraction [J]. Computer Science, 2023, 50(5): 248-254.
[14] BAI Xuefei, JIN Zhichao, WANG Wenjian, MA Yanan. Skin Lesion Segmentation Combining Boundary Enhancement and Multi-scale Attention [J]. Computer Science, 2023, 50(4): 96-102.
[15] YIN Haitao, WANG Tianyou. Image Denoising Algorithm Based on Deep Multi-scale Convolution Sparse Coding [J]. Computer Science, 2023, 50(4): 133-140.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!