Enhancing Supply Chain Defect Detection through Cross-Domain Generalization: A Deep Learning Approach

Sujin Yli; Deokyoon Choi

doi:10.54097/6kybyzv5

Authors

Sujin Yli
Deokyoon Choi

DOI:

https://doi.org/10.54097/6kybyzv5

Keywords:

Deep learning, Supply chain, Defect detection, Cross-domain generalization

Abstract

Ensuring quality control in the supply chain is paramount for maintaining product integrity and customer satisfaction. However, the scarcity of defect-specific data poses significant challenges for effective defect detection using traditional machine learning models. This paper introduces a novel Cross-Domain Generalization (CDG) methodology that integrates Cross-domain Augmentation, Multi-task Learning, and Iteration Learning to address data limitations and improve defect detection accuracy. Leveraging datasets from related domains, our approach enhances model generalization and robustness. Experimental results demonstrate substantial performance improvements over baseline methods, highlighting the potential of CDG in various industrial applications. The proposed methodology offers a scalable solution for supply chain quality control, enabling more reliable and efficient defect detection.

References

Y. Chen, Y. Ding, F. Zhao, E. Zhang, Z. Wu, and L. Shao, "Surface defect detection methods for industrial products: A review," Applied Sciences, vol. 11, no. 16, p. 7657, 2021.

Z. Ma, X. Chen, T. Sun, X. Wang, Y. C. Wu, and M. Zhou, "Blockchain-Based Zero-Trust Supply Chain Security Integrated with Deep Reinforcement Learning for Inventory Optimization," Future Internet, vol. 16, no. 5, p. 163, 2024.

H. Di, X. Ke, Z. Peng, and Z. Dongdong, "Surface defect classification of steels with a new semi-supervised learning method," Optics and Lasers in Engineering, vol. 117, pp. 40-48, 2019.

I. Konovalenko, P. Maruschak, J. Brezinová, J. Viňáš, and J. Brezina, "Steel surface defect classification using deep residual neural network," Metals, vol. 10, no. 6, p. 846, 2020.

Y. Wang, L. Gao, Y. Gao, and X. Li, "A new graph-based semi-supervised method for surface defect classification," Robotics and Computer-Integrated Manufacturing, vol. 68, p. 102083, 2021.

Z. He, and Q. Liu, "Deep regression neural network for industrial surface defect detection," IEEE Access, vol. 8, pp. 35583-35591, 2020.

H. K. Dandage, K.-M. Lin, H.-H. Lin, Y.-J. Chen, and K.-S. Tseng, "Surface defect detection of cylindrical lithium-ion battery by multiscale image augmentation and classification," International Journal of Modern Physics B, vol. 35, no. 14-16, p. 2140011, 2021.

X. Li, Y. Zheng, B. Chen, and E. Zheng, "Dual attention-based industrial surface defect detection with consistency loss," Sensors, vol. 22, no. 14, p. 5141, 2022.

R. Cohn, and E. Holm, "Unsupervised machine learning via transfer learning and k-means clustering to classify materials image data," Integrating Materials and Manufacturing Innovation, vol. 10, no. 2, pp. 231-244, 2021.

X. Liu, L. Wu, X. Guo, D. Andriukaitis, G. Królczyk, and Z. Li, "A novel approach for surface defect detection of lithium battery based on improved K-nearest neighbor and Euclidean clustering segmentation," The International Journal of Advanced Manufacturing Technology, vol. 127, no. 1-2, pp. 971-985, 2023.

C. Xu, L. Li, J. Li, and C. Wen, "Surface defects detection and identification of lithium battery pole piece based on multi-feature fusion and PSO-SVM," IEEE Access, vol. 9, pp. 85232-85239, 2021.

Y. Liu, Y. Chen, and J. Xu, "An automatic defects detection scheme for lithium-ion battery electrode surface," in Proceedings of the International Symposium on Automation Systems (ISAS), 2020, pp. 94-99.

Y. Chen, Y. Shu, X. Li, C. Xiong, S. Cao, X. Wen, and Z. Xie, "Research on detection algorithm of lithium battery surface defects based on embedded machine vision," Journal of Intelligent & Fuzzy Systems, vol. 41, no. 3, pp. 4327-4335, 2021.

H. F. Le, L. J. Zhang, and Y. X. Liu, "Surface defect detection of industrial parts based on YOLOV5," IEEE Access, vol. 10, pp. 130784-130794, 2022.

X. Chen, M. Liu, Y. Niu, X. Wang, and Y. C. Wu, "Deep-Learning-Based Lithium Battery Defect Detection via Cross-Domain Generalization," IEEE Access, vol. 12, pp. 78505-78514, 2024.

Y. Chen, Y. Ding, F. Zhao, E. Zhang, Z. Wu, and L. Shao, "Surface defect detection methods for industrial products: A review," Applied Sciences, vol. 11, no. 16, p. 7657, 2021.

X. Zheng, S. Zheng, Y. Kong, and J. Chen, "Recent advances in surface defect inspection of industrial products using deep learning techniques," The International Journal of Advanced Manufacturing Technology, vol. 113, no. 1-2, pp. 35-58, 2021.

H. Di, X. Ke, Z. Peng, and Z. Dongdong, "Surface defect classification of steels with a new semi-supervised learning method," Optics and Lasers in Engineering, vol. 117, pp. 40-48, 2019.

I. Konovalenko, P. Maruschak, J. Brezinová, J. Viňás, and J. Brezina, "Steel surface defect classification using deep residual neural network," Metals, vol. 10, no. 6, p. 846, 2020.

Y. Wang, L. Gao, Y. Gao, and X. Li, "A new graph-based semi-supervised method for surface defect classification," Robotics and Computer-Integrated Manufacturing, vol. 68, p. 102083, 2021.

A. Dosovitskiy et al., "An image is worth 16x16 words: Transformers for image recognition at scale," arXiv preprint arXiv:2010.11929, 2020.

X. Li, Y. Zheng, B. Chen, and E. Zheng, "Dual attention-based industrial surface defect detection with consistency loss," Sensors, vol. 22, no. 14, p. 5141, 2022.

Y. Zhou, X. Geng, T. Shen, W. Zhang, and D. Jiang, "Improving zero-shot cross-lingual transfer for multilingual question answering over knowledge graph," in Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021, pp. 5822-5834.

Y. Zhou, T. Shen, X. Geng, G. Long, and D. Jiang, "ClarET: Pre-training a correlation-aware context-to-event transformer for event-centric generation and classification," in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, 2022, pp. 2559-2575.

X. Zheng, S. Zheng, Y. Kong, and J. Chen, "Recent advances in surface defect inspection of industrial products using deep learning techniques," The International Journal of Advanced Manufacturing Technology, vol. 113, no. 1-2, pp. 35-58, 2021.

K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770-778.

M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, "MobileNetV2: Inverted residuals and linear bottlenecks," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 4510-4520.

A. Dosovitskiy et al., "An image is worth 16x16 words: Transformers for image recognition at scale," arXiv preprint arXiv:2010.11929, 2020.

Z. Mlika, and S. Cherkaoui, "Network slicing with MEC and deep reinforcement learning for the Internet of Vehicles," IEEE Network, vol. 35, no. 3, pp. 132-138, 2021.

H. Moudoud, and S. Cherkaoui, "Empowering Security and Trust in 5G and Beyond: A Deep Reinforcement Learning Approach," IEEE Open Journal of the Communications Society, vol. 4, pp. 2410-2420, 2023.

Y. Chen, Y. Ding, F. Zhao, E. Zhang, Z. Wu, and L. Shao, "Surface defect detection methods for industrial products: A review," Applied Sciences, vol. 11, no. 16, p. 7657, 2021.

M. Ohm, L. Kempf, F. Boes, and M. Meier, "Supporting the detection of software supply chain attacks through unsupervised signature generation," arXiv preprint arXiv:2011.02235, 2020.

S. Ismail, and H. Reza, "Security Challenges of Blockchain-Based Supply Chain Systems," in Proceedings of the 2022 IEEE 13th Annual Ubiquitous Computing Electronics & Mobile Communication Conference (UEMCON), 2022, pp. 1-6.

S. A. Melnyk, U. Bititci, K. Platts, J. Tobias, and B. Andersen, "Is performance measurement and management fit for the future?" Management Accounting Research, vol. 25, no. 2, pp. 173-186, 2014.

Z. He, and Q. Liu, "Deep regression neural network for industrial surface defect detection," IEEE Access, vol. 8, pp. 35583-35591, 2020.

H. K. Dandage, K.-M. Lin, H.-H. Lin, Y.-J. Chen, and K.-S. Tseng, "Surface defect detection of cylindrical lithium-ion battery by multiscale image augmentation and classification," International Journal of Modern Physics B, vol. 35, no. 14-16, p. 2140011, 2021.

X. Li, Y. Zheng, B. Chen, and E. Zheng, "Dual attention-based industrial surface defect detection with consistency loss," Sensors, vol. 22, no. 14, p. 5141, 2022.

R. Cohn, and E. Holm, "Unsupervised machine learning via transfer learning and k-means clustering to classify materials image data," Integrating Materials and Manufacturing Innovation, vol. 10, no. 2, pp. 231-244, 2021.

X. Liu, L. Wu, X. Guo, D. Andriukaitis, G. Królczyk, and Z. Li, "A novel approach for surface defect detection of lithium battery based on improved K-nearest neighbor and Euclidean clustering segmentation," The International Journal of Advanced Manufacturing Technology, vol. 127, no. 1-2, pp. 971-985, 2023.

H. F. Le, L. J. Zhang, and Y. X. Liu, "Surface defect detection of industrial parts based on YOLOV5," IEEE Access, vol. 10, pp. 130784-130794, 2022.

K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770-778.

Wang, X., Wu, Y. C., & Ma, Z. (2024). Blockchain in the courtroom: exploring its evidentiary significance and procedural implications in US judicial processes. Frontiers in Blockchain, 7, 1306058.

K. Simonyan, and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.

M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, "MobileNetV2: Inverted residuals and linear bottlenecks," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 4510-4520.

Wang, X., Wu, Y. C., Ji, X., & Fu, H. (2024). Algorithmic discrimination: examining its types and regulatory measures with emphasis on US legal practices. Frontiers in Artificial Intelligence, 7, 1320277.

Y. Chen, Y. Ding, F. Zhao, E. Zhang, Z. Wu, and L. Shao, "Surface defect detection methods for industrial products: A review," Applied Sciences, vol. 11, no. 16, p. 7657, 2021.