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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Peng, C., Zhang, X., Yu, G., Luo, G., Sun, J.: Large Kernel matters —— improve semantic segmentation by global convolutional network. arXiv:1703.02719(2017)
Dosovitskiy, A.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv:2010.11929 (2020)
Xie, E., et al.: SegFormer: simple and efficient design for semantic segmentation with transformers. arXiv 2105.15203(2021)
Liu, Z., et al.: A ConvNet for the 2020s. arXiv:2201.03545 (2022)
PyTorch Homepage, https://pytorch.org/. Accessed 30 Oct 2022
mmsegmentation Homepage. https://github.com/open-mmlab/mmsegmentation. Accessed 30 Oct 2022
ONNX Runtime Homepage. https://onnxruntime.ai/. Accessed 30 Oct 2022
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-031-23585-6_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-23584-9
Online ISBN: 978-3-031-23585-6
eBook Packages: Computer ScienceComputer Science (R0)