Lithium battery electrode surface defect detection method based on background standardization and centralized compensation algorithm

Abstract

The invention discloses a lithium battery electrode surface defect detection method based on background standardization and a centralized compensation algorithm, being implemented by a measurement system constructed by lighting equipment, a linear array camera, a conveying device, a labeling machine, an industrial personal computer and a PLC. The lithium battery electrode surface defect detectionmethod comprises the following specific implementation steps: acquiring a lithium battery electrode surface image by using the linear array camera; preprocessing the electrode surface image by using abackground standardization algorithm; carrying out defect rough detection, and carrying out automatic threshold segmentation on the preprocessed electrode surface image to obtain a possible defect area; and finally, carrying out accurate defect detection, and carrying out accurate detection on areas where defects possibly exist by using an automatic centralized compensation algorithm so as to obtain defect images. According to the lithium battery electrode surface defect detection method provided by the invention, on the premise of ensuring the lithium battery electrode surface defect detection effect, the requirement of online real-time defect detection in the actual lithium battery industrial production process can be met, and the production efficiency of the lithium battery is improved.

Description

technical field [0001] The invention relates to the technical field of machine vision detection, in particular to a method for detecting surface defects of lithium battery electrodes based on background standardization and centralized compensation algorithms. Background technique [0002] Due to the advantages of high energy density and fast charging and discharging speed, lithium-ion batteries occupy a dominant position in the portable electronic communication equipment market, and gradually expand to the field of electric tools. At present, electrical appliances requiring portable power such as mobile phones, notebook computers, and micro-cameras have become an indispensable part of people's lives. In terms of power supplies, lithium-ion batteries are selected as the mainstream of the market without exception. According to statistics, the global mobile phone production is nearly 2.1 billion units per year, and the global annual production of notebook computers is about 150...

AI Technical Summary

Problems solved by technology

[0004] At present, there are many defect detection methods based on machine vision, and some methods are only suitable for defects with obvious gray value differences with the surrounding background; some defect detection methods have good detection results in actual situations, but are sensitive to noise and take too much time For example, a defect detection method in the frequency domain—a defect detection method based on Fourier transform combined with various filters such as Gaussian filters, this method can remove the texture background and make the defect become Obviously, but the amount of calculation is relatively large, and the calculation time is long, which cannot meet the requirements of online real-time detection in industry; in order to meet the requirements of online real-time detection in industrial production, researchers in related fields have proposed some methods to speed up defect detection. Algorithms such as Fast Fourier Transform (FFT) algorithm, Fast Regularized Metric (FRM) algorithm, Local Neighborhood Analysis (LNA) algorithm, etc.

However, the current detection methods used in the actual lithium battery industrial production for the detection of lithium battery electrode surface defects are not very mature. Well, even some small and medium-sized enterprises mainly use manual inspection to detect defects on the surface of lithium battery electrodes.

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