Lithium battery surface defect detection method based on generative adversarial network
A detection method and defect detection technology, applied in biological neural network models, neural learning methods, image analysis, etc., can solve the problems of relying on a large amount of label data, insufficient detection methods, waste of manpower and material resources, etc.
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[0057] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.
[0058] refer to image 3 as shown, image 3 For the steps of the detection method of the present invention,
[0059] A lithium battery surface defect detection method based on generative confrontation network, the method is divided into two steps:
[0060] The first part, get the surface distribution of lithium-ion battery
[0061] 1-1. Image collection: Use a high-precision CMOS industrial camera to collect images of the normal surface of the...
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