A defect detection method for lithium batteries based on shear wave threshold and K-means clustering segmentation

Xiyang Pan; Linsheng Li

doi:10.1117/12.3025970

15 March 2024 A defect detection method for lithium batteries based on shear wave threshold and K-means clustering segmentation

Xiyang Pan, Linsheng Li

Author Affiliations +

Proceedings Volume 13075, Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023); 1307522 (2024) https://doi.org/10.1117/12.3025970
Event: Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023), 2023, Kunming, China

Abstract

Aiming at the problems of slow labor efficiency and low precision of lithium battery defect detection, a new method based on shear wave threshold and K-means clustering segmentation was proposed. For the defect image, the ROI region of interest was first extracted to obtain the polar slice region containing only the defect parts. Then, the image denoising was carried out with the shear wave threshold and the histogram normalization was carried out with image enhancement processing to obtain the image with low noise and high contrast between the defect and the background. The defect was extracted by K-means clustering segmentation algorithm. Morphological corrosion and expansion operations were carried out to fill the defects. Finally, the prewitt edge detection operator was used to detect the defects and output the defect information. The experimental results show that the algorithm can detect metal leakage, damage, black spots and scratches very well, which is suitable for industrial production.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiyang Pan and Linsheng Li "A defect detection method for lithium batteries based on shear wave threshold and K-means clustering segmentation", Proc. SPIE 13075, Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023), 1307522 (15 March 2024); https://doi.org/10.1117/12.3025970

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KEYWORDS

Image segmentation

Gaussian filters

Defect detection

Tunable filters

Denoising

Image processing algorithms and systems

Lithium

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