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Deep Learning-Based Visual Defect Inspection System for Pouch Battery Packs

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Cognitive Computing – ICCC 2022 (ICCC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13734))

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Abstract

Every year, billions of pouch battery packs are produced worldwide. The production of pouch battery packs has been highly automated. However, visual inspection is the last step of production that still requires a large number of workers. We present the hardware and software design of an automated visual inspection system for pouch battery packs. We have achieved a 4% false alarm rate, 0.7% missing alarm rate, and 3.5 s cycle time on this challenging task through well-designed optical hardware and the latest deep learning techniques.

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References

  1. Peng, C., Zhang, X., Yu, G., Luo, G., Sun, J.: Large Kernel matters —— improve semantic segmentation by global convolutional network. arXiv:1703.02719(2017)

  2. Dosovitskiy, A.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv:2010.11929 (2020)

  3. Xie, E., et al.: SegFormer: simple and efficient design for semantic segmentation with transformers. arXiv 2105.15203(2021)

  4. Liu, Z., et al.: A ConvNet for the 2020s. arXiv:2201.03545 (2022)

  5. PyTorch Homepage, https://pytorch.org/. Accessed 30 Oct 2022

  6. mmsegmentation Homepage. https://github.com/open-mmlab/mmsegmentation. Accessed 30 Oct 2022

  7. ONNX Runtime Homepage. https://onnxruntime.ai/. Accessed 30 Oct 2022

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Correspondence to Xu Wang .

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Wang, X., Cheng, P. (2022). Deep Learning-Based Visual Defect Inspection System for Pouch Battery Packs. In: Yang, Y., Wang, X., Zhang, LJ. (eds) Cognitive Computing – ICCC 2022. ICCC 2022. Lecture Notes in Computer Science, vol 13734. Springer, Cham. https://doi.org/10.1007/978-3-031-23585-6_5

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  • DOI: https://doi.org/10.1007/978-3-031-23585-6_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23584-9

  • Online ISBN: 978-3-031-23585-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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