建構深度學習CNN模型以正確分類傳統AOI模型之偵測結果

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姓名

芮妮雅(Rania Akhmalia) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

建構深度學習CNN模型以正確分類傳統AOI模型之偵測結果
(Building CNN Model to Reclassify Overkill Fabrics from Company′s AOI Defect Results)

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摘要(中)

宏遠是一家紡織廠商，利用AOI來偵測紡織布上的瑕疵。然而目前AOI無法有效解決他們所遭遇的問題，主要是因為「瑕疵」還能夠細分成多個種類。AOI使用三個反射光相機與三個穿透光相機，依據AOI的規則，擷取出多種瑕疵種類，如：摺痕、斷線、髒污、脫線、白點等。然而宏遠卻不同於AOI規則，對瑕疵種類有不同的定義，他們認為的瑕疵如：破洞、斷經等種類。因為兩種瑕疵定不同，使得AOI偵測出的瑕疵影像造成多達90%的誤判，因此宏遠專業的檢測員需要重新將誤判影像分類成有瑕疵與無瑕疵。而我們的目標則是要建立深度學習的模型，來減少檢測員花在重新分類上的勞力。這項作業使用Autoencoder來重建資料集，再利用CNN進行分類。實驗的結果顯示我們的模型能夠減少檢測員高達80%以上的勞力耗費，同時保有FNR小於5%與FPR小於15%的表現。

摘要(英)

The textile company applied AOI to detect a defect on fabric automatically. In their case, AOI failed to overcome this problem because there are differences defect categories. AOI has defect with categories among others; fold, black thread, broken thread, hooked thread, needle mark, mosquito, stain, thread-off, white spot and uneven thickness. Whereas, Textile company has different defects categories, its categories among others; Thinning, missing weft, stop mark, weft mark, loose weft, warp break, mechanical section and fold back. These differences caused more than 90% of captured images are not defect (overkill). A Professional Inspector in Textile Company reclassified those overkill images into real defect and non-defect images. Our goal in this work is, to reduce a Professional Inspector working loads by proposed two approaches. The first approach is, building a tiny and light CNN architecture as classifier model. While second approach is, combining Autoencoder to reconstruct the dataset as an input to CNN model that built in first approach. The result shows that the models are able to reduce a Professional Inspector working loads up to 90% with maximum FNR 5% and FPR less than 5%.

關鍵字(中)

★ 瑕疵

關鍵字(英)

★ defect
★ CNN
★ AOI

論文目次

摘要 v
ABSTRACT vi
ACKNOWLEDGMENT vii
TABLE OF CONTENTS viii
LIST OF FIGURES x
LIST OF TABLES xii
CHAPTER 1 INTRODUCTION 1
1.1 Background 1
1.2 Motivation 3
1.3 Research Objective 4
1.4 Research Contributions 4
1.5 Thesis Structure 4
CHAPTER 2 LITERATURE REVIEW 6
2.1 Defect Issues on Fabric 6
2.2 Convolutional Neural Network (CNN) 8
2.3 Autoencoder (AE) 10
2.4 Convolutional Autoencoder (CAE) 10
CHAPTER 3 EXPERIMENT 12
3.1 Experiment Scenario 13
3.1.1 Approach I 14
3.1.2 Approach II 16
3.2 Data Collection and Pre-processing 19
3.3 CNN Architecture 24
3.4 Convolutional Autoencoder (CAE) Architecture 26
3.4.1 Convolutional Autoencoder’s Pre-Processing 26
3.4.2 Convolutional Autoencoder’s Architecture Network 27
CHAPTER 4 RESULT 29
4.1 Performance Measurements 29
4.2 Approach 1 Result 30
4.3 Approach 2 Result 31
CHAPTER 5 CONCLUSION AND FUTURE WORKS 33
5.1 Conclusion 33
5.2 Future Works 33
BIBLIOGRAPHY 34

參考文獻

[1] Abdel Salam Malek, “Online fabric inspection by image processing technology,” Other. Universite de Haute Alsace - Mulhouse, 2012. English. ffNNT : 2012MULH4090ff. fftel-00720041ff
[2] Bahera, BK. Mni, MP, “Characterization and classification of fabric defects using discrete cosine transformation and artificial neural network” in Indian Journal of Fibre & Textile Research (IJFTR), vol. 34, no. 4, pp. 421-426, Dec. 2007
[3] H. Xie, Y. Zhang and Z. Wu, "Fabric Defect Detection Method Combing Image Pyramid and Direction Template," in IEEE Access, vol. 7, pp. 182320-182334, 2019, doi: 10.1109/ACCESS.2019.2959880.
[4] J. Masci, U. Meier, D. Cire?an, and J. Schmidhuber, “Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction,” Lecture Notes in Computer Science Artificial Neural Networks and Machine Learning – ICANN 2011, pp. 52–59, 2011.
[5] Jia-Jyun Syue and Hsien-Huang Wu, "Defect Detection of Golf Ball Surface Based on Deep Learning," 2019 CVGIP Computer Vision Graphics and Image Processing Workshop, Taitung, Taiwan, 2019
[6] Keras Community and Governance, Keras Documentation, [Online]. Available: https://keras.io/
[7] L. Weninger, M. Kopaczka and D. Merhof, "Defect detection in plain weave fabrics by yarn tracking and fully convolutional networks," 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Houston, TX, 2018, pp. 1-6.
[8] Liu, Z., Zhang, C., Li, C., Ding, S., Dong, Y., & Huang, Y. (2019). Fabric defect recognition using optimized neural networks. Journal of Engineered Fibers and Fabrics. https://doi.org/10.1177/1558925019897396
[9] M. Polic, I. Krajacic, N. Lepora and M. Orsag, "Convolutional Autoencoder for Feature Extraction in Tactile Sensing," in IEEE Robotics and Automation Letters, vol. 4, no. 4, pp. 3671-3678, Oct. 2019, doi: 10.1109/LRA.2019.2927950.
[10] Mei, S.; Wang, Y.; Wen, G., “Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model”, Sensors 2018, 18, 1064.
[11] Q. V. Le, “A tutorial on deep learning part 2: Autoencoders convolutional neural networks and recurrent neural networks”, 2015, [online] Available: https://cs.stanford.edu/~quocle/tutorial2.pdf
[12] Qinxue Meng, Daniel Catchpoole, David Skillicorn, and Paul J Kennedy. “Relational autoencoder for feature extraction,” arXiv preprint arXiv:1802.03145, 2018.
[13] S. Shirmohammadi and A. Ferrero, "Camera as the instrument: The rising trend of vision based measurement", IEEE Instrum. Meas. Mag., vol. 17, no. 3, pp. 41-47, Jun. 2014
[14] Tao, X.; Zhang, D.; Ma, W.; Liu, X.; Xu, D. “Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks,” Appl. Sci. 2018, 8, 1575.
[15] W. Wang, S. Chen, L. Chen and W. Chang, "A Machine Vision Based Automatic Optical Inspection System for Measuring Drilling Quality of Printed Circuit Boards," in IEEE Access, vol. 5, pp. 10817-10833, 2017, doi: 10.1109/ACCESS.2016.2631658.
[16] Y. Zhang, “A better autoencoder for image: Convolutional autoencoder,” in ICONIP17-DCEC, 2018. [Online]. Available: http://users.cecs.anu.edu. au/Tom.Gedeon/conf/ABCs2018/paper/ABCs2018_paper_58.pdf, Accessed on Mar. 23 2017
[17] Yamashita, R., Nishio, M., Do, R.K.G. et al. “Convolutional neural networks: an overview and application in radiology,” Insights Imaging 9, 611–629 (2018). https://doi.org/10.1007/s13244-018-0639-9

指導教授

梁德容張欽圳(Deron Liang Chin-Chun Chang)

審核日期

2020-7-30

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