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Semantic segmentation supervised deep-learning algorithm for welding-defect detection of new energy batteries

  • S.I. : Deep learning modelling in real life: (Anomaly Detection, Biomedical, Concept Analysis, Finance, Image analysis, Recommendation)
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Abstract

As the main component of the new energy battery, the safety vent usually is welded on the battery plate, which can prevent unpredictable explosion accidents caused by the increasing internal pressure of the battery. The welding quality of safety vent directly affects the safety and stability of the battery; so, the welding-defect detection is of great significance. In this paper, we researched the welding-defect detection method based on semantic segmentation algorithm. The automatic detection method should recognize, locate, and count the area of defects. However, small differences between inter-classes, strong variations in defect size, and uncertain annotation create challenges for defect semantic segmentation. To address these challenges, a customized deep learning model based on two-branch architecture is proposed to segment welding-defect at the pixel level. This architecture involves three modules as follows, (i) The Spatial Branch is elaborately designed to capture spatial details and generate high-resolution feature representation; (ii) The Context Branch is responsible for obtaining semantic context; (iii) The Feature Fusion Block is proposed to merge the two types of feature representation and enhance mutual connections. Furthermore, a welding image dataset was built to validate our method, and the network’s performance of defect segmentation and classification were evaluated by the defects images and the normal images, respectively. Data augmentation strategies are performed to overcome the problem of imbalanced distribution and inaccurate labels of the dataset, which is caused by the ambiguity of defect edges and the subjectivity of manual annotation. The experiment results indicate that our method achieves 86.704\(\%\) of mIoU (mean intersection of union), which supports the effectiveness of the deep learning model in segmenting defects. Besides, 98.896\(\%\) of AP (average precision) and 0\(\%\) of MDR (miss detection rate) further suggests the applicability of the proposed framework in industrial applications.

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References

  1. Ren G, Meng Y, Shao B, Liu T (2016) Analysis in secondary use of new energy automotive battery. Adv Energy Power Eng 4:82–87

    Article  Google Scholar 

  2. Cao X, Wallace W, Poon C, Immarigeon J-P (2003) Research and progress in laser welding of wrought aluminum alloys. i. laser welding processes. Mater Manuf Process 18(1):1–22

  3. Jiang B, Wang C, Hsu Y (2007) Machine vision and background remover-based approach for pcb solder joints inspection. Int J Prod Res 45(2):451–464

    Article  Google Scholar 

  4. Chen B, Fang Z, Xia Y, Zhang L, Huang Y, Wang L (2018) Accurate defect detection via sparsity reconstruction for weld radiographs. NDT E Int 94:62–69

    Article  Google Scholar 

  5. Rossa R, Borella A, Giani N (2020) Comparison of machine learning models for the detection of partial defects in spent nuclear fuel. Ann Nuclear Energy 147:107680

    Article  Google Scholar 

  6. Li B, Pi D (2020) Network representation learning: a systematic literature review. Neural Comput Appl 32(21):16647–16679

    Article  Google Scholar 

  7. Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: European conference on computer vision, pp 818–833. Springer

  8. Park J-K, Kwon B-K, Park J-H, Kang D-J (2016) Machine learning-based imaging system for surface defect inspection. Int J Precis Eng Manuf Green Technol 3(3):303–310

    Article  Google Scholar 

  9. Cha Y-J, Choi W, Büyüköztürk O (2017) Deep learning-based crack damage detection using convolutional neural networks. Comput Aided Civil Infrastruct Eng 32(5):361–378

    Article  Google Scholar 

  10. Yuan H, Chen H, Liu S, Lin J, Luo X (2019) A deep convolutional neural network for detection of rail surface defect. In: 2019 IEEE vehicle power and propulsion conference (VPPC), pp 1–4. IEEE

  11. Chen J, Liu Z, Wang H, Nunez A, Han Z (2017) Automatic defect detection of fasteners on the catenary support device using deep convolutional neural network. IEEE Trans Instrument Measure 67(2):257–269

    Article  Google Scholar 

  12. Shu Y, Huang Y, Li B (2019) Design of deep learning accelerated algorithm for online recognition of industrial products defects. Neural Comput Appl 31(9):4527–4540

    Article  Google Scholar 

  13. He D, Xu K, Zhou P (2019) Defect detection of hot rolled steels with a new object detection framework called classification priority network. Comput Ind Eng 128:290–297

    Article  Google Scholar 

  14. Volkau I, Mujeeb A, Wenting D, Marius E, Alexei S (2019) Detection defect in printed circuit boards using unsupervised feature extraction upon transfer learning. In: 2019 International conference on cyberworlds (CW), pp 101–108. IEEE

  15. Ren S, He K Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. arXiv preprint arXiv:1506.01497

  16. Ostu N (2007) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cyber 9(1):62–66

    Google Scholar 

  17. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440

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

  19. Jain V, Seung HS, Turaga SC (2010) Machines that learn to segment images: a crucial technology for connectomics. Current Opin Neurobiol 20(5):653–666

    Article  Google Scholar 

  20. Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention, pp 234–241. Springer

  21. Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell 40(4):834–848

    Article  Google Scholar 

  22. Zhao H, Shi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2881–2890

  23. Huang Z, Wang X, Huang L, Huang C, Wei Y, Liu W (2019) Ccnet: Criss-cross attention for semantic segmentation. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 603–612

  24. Fu J, Liu J, Tian H, Li Y, Bao Y, Fang Z, Lu H (2019) Dual attention network for scene segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 3146–3154

  25. Yuan Y, Huang L, Guo J, Zhang C, Chen X, Wang J (2018) Ocnet: Object context network for scene parsing. arXiv preprint arXiv:1809.00916

  26. Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481–2495

    Article  Google Scholar 

  27. Canziani A, Paszke A, Culurciello E (2016) An analysis of deep neural network models for practical applications. arXiv preprint arXiv:1605.07678

  28. Zhao H, Qi X, Shen X, Shi J, Jia J (2018) Icnet for real-time semantic segmentation on high-resolution images. In: Proceedings of the European conference on computer vision (ECCV), pp 405–420

  29. Wu T, Tang S, Zhang R, Cao J, Zhang Y (2020) Cgnet: a light-weight context guided network for semantic segmentation. IEEE Trans Image Process 30:1169–1179

    Article  Google Scholar 

  30. Yu C, Wang J, Peng C, Gao C, Yu G, Sang N (2018) Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European conference on computer vision (ECCV), pp 325–341

  31. Zhang H, Di X, Zhang Y (2020) Real-time cu-net-based welding quality inspection algorithm in battery production. IEEE Trans Ind Electron 67(12):10942–10950

    Article  Google Scholar 

  32. Wang Y, Zhou Q, Liu J, Xiong J, Gao G, Wu X, Latecki LJ (2019) Lednet: A lightweight encoder-decoder network for real-time semantic segmentation. In: 2019 IEEE international conference on image processing (ICIP), pp 1860–1864. IEEE

  33. Yang Y, Pan L, Ma J, Yang R, Zhu Y, Yang Y, Zhang L (2020) A high-performance deep learning algorithm for the automated optical inspection of laser welding. Appl Sci 10(3):933

    Article  Google Scholar 

  34. Yang Y, Yang R, Pan L, Ma J, Zhu Y, Diao T, Zhang L (2020) A lightweight deep learning algorithm for inspection of laser welding defects on safety vent of power battery. Comput Ind 123:13306

    Article  Google Scholar 

  35. Zhu Y, Yang R, He Y, Ma J, Guo H, Yang Y, Zhang L (2021) A lightweight multiscale attention semantic segmentation algorithm for detecting laser welding defects on safety vent of power battery. IEEE Access 9:39245–39254

    Article  Google Scholar 

  36. Yu H, Yang Z, Tan L, Wang Y, Sun W, Sun M, Tang Y (2018) Methods and datasets on semantic segmentation: a review. Neurocomputing 304:82–103

    Article  Google Scholar 

  37. Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition, pp 248–255. IEEE

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Acknowledgements

This research was partially supported by the Shenzhen Science and Technology Program under Grant JCYJ20210324093806017, and the Shenzhen-Hong Kong Joint Innovation Foundation under Grant SGDX20190919094401725.

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    Authors and Affiliations

    1. College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, China

      Yatao Yang, Yuqing He, Haolin Guo, Ziliang Chen & Li Zhang

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    1. Yatao Yang
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    Contributions

    YY: Conceptualization, Methodology. YH: Data Curation, Writing—Original Draft, Software. HG: Resources.ZC: Investigation and Validation. ZC: Investigation and Validation. LZh: Supervision, Writing—Review and Editing.

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    Correspondence to Li Zhang.

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    We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work. There is no professional or other personal interest of any nature or kind in any product, service or company that could be constructed as influencing the position presented in, or the review of, the manuscript entitled “Semantic Segmentation Supervised Deep-Learning Algorithm for Welding-Defect Detection of New Energy Battery”.

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    Yang, Y., He, Y., Guo, H. et al. Semantic segmentation supervised deep-learning algorithm for welding-defect detection of new energy batteries. Neural Comput & Applic 34, 19471–19484 (2022). https://doi.org/10.1007/s00521-022-07474-0

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