Method of detecting and classifying defects and electronic device using the same

An electronic device comprising:

• a processor;
• and
• a non-transitory storage medium coupled to the processor and configured to store a plurality of instructions, which cause the processor to:
• input an image to be detected to a trained autoencoder to obtain a reconstructed image corresponding to the image to be detected;
• determine whether the image to be detected has defects based on a defect criteria for filtering out small noise reconstruction errors;
• in response that the image to be detected has defects, calculate a plurality of structural similarity values between the image to be detected and a plurality of template images marking defect categories respectively;
• determine a target defect category corresponding to the template image with the highest structural similarity value, and classifying the image to be detected into the target defect category, wherein a mathematical expression of the defect criteria is
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A non-transitory storage medium having stored thereon instructions that, when executed by at least one processor of an electronic device, causes the least one processor to execute instructions of a method for detecting and classifying defects, the method comprising:

• inputting an image to be detected to a trained autoencoder to obtain a reconstructed image corresponding to the image to be detected;
• determining whether the image to be detected has defects based on a defect criteria for filtering out small noise reconstruction errors;
• in response that the image to be detected has defects, calculating a plurality of structural similarity values between the image to be detected and a plurality of template images marking defect categories respectively;
• determining a target defect category corresponding to the template image with the highest structural similarity value, and classifying the image to be detected into the target defect category, wherein a mathematical expression of the defect criteria is
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• wherein ΔXi,j is a reconstruction error image between the image to be detected and the reconstructed image, δXi,j is a binary image of the reconstruction error image, i and j represent pixel positions, τ is a preset reconstruction error measurement threshold.