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Lithium battery electrode surface defect classification method

A technology of electrode surface and classification method, which is applied in the direction of instruments, character and pattern recognition, computer components, etc., to achieve the effect of improving accuracy

Pending Publication Date: 2021-11-19
SOUTH CHINA UNIV OF TECH +1
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

Therefore, it is somewhat challenging to accurately classify these six types of defects

Method used

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  • Lithium battery electrode surface defect classification method
  • Lithium battery electrode surface defect classification method
  • Lithium battery electrode surface defect classification method

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Embodiment

[0068] This embodiment mainly proposes a classification method for the surface defects of lithium battery electrodes, which is used to classify bright defects and dark defects on the surface of lithium battery electrodes. Among them, bright defects include bright spots, metal leakage, and bubbles, and dark defects include black spots. , streaks, decarburization. The classification method first uses the change of the gray value of the specified area, combined with the ring mask method to classify the six types of defect images into bright defect data sets and dark defect data sets; The extracted features are subdivided into the two categories of bright defect data set and dark defect data set to obtain the final classification result.

[0069] figure 1 It is a flow chart of a method for classifying surface defects of lithium battery electrodes disclosed in this embodiment, which will be described through specific embodiments below. The classification method in this embodiment...

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Abstract

The invention discloses a lithium battery electrode surface defect classification method. The method comprises the following steps: firstly, dividing a defect image into a bright defect data set and a dark defect data set according to gray value change of a specified area of the defect image and pixel distribution in a specific annular area obtained according to a defect shape; then, extracting features of the image by using a Gabor filter, and carrying out further detailed classification on the two categories by using a random forest classifier. According to the method, six types of lithium battery electrode surface defects can be accurately classified, and the method has good robustness and rapidity.

Description

technical field [0001] The invention relates to the technical field of machine vision detection, in particular to a method for classifying surface defects of lithium battery electrodes. Background technique [0002] At present, lithium batteries have attracted more and more attention in fields such as electric vehicles due to their advantages such as high energy density, long life, small size, and no pollution. However, the safety hazards behind the convenience brought by lithium batteries to human life are also worthy of attention. In addition to the internal structure of the lithium battery and other reasons, the surface defects of the electrodes generated during the production process will also cause certain safety hazards. Therefore, the defect detection of lithium batteries is particularly important. It is worth noting that efficient and accurate defect classification can in turn greatly facilitate defect detection. [0003] Nowadays, many researchers focus their res...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/46
CPCG06F18/2411G06F18/24323G06F18/214
Inventor 刘屿倪君仪徐嘉明万伟伟横井浩史
Owner SOUTH CHINA UNIV OF TECH
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