Method and device for detecting defects, electronic device using method, and non-transitory storage medium

An electronic device comprising:

• a storage device;
• at least one processor;
• and
• the storage device storing one or more programs, which when executed by the at least one processor, cause the at least one processor to:
• obtain a trained weighting generated when an autoencoder and pixel convolutional neural network is being trained with a plurality of defect-free training samples;
• input the trained weighting into the autoencoder and pixel convolutional neural network to obtain a test encoding feature;
• the test encoding feature is obtained by encoding test samples using a weighted autoencoder of the weighted autoencoder and pixel convolutional neural network;
• the test encoding feature is a feature generated when the test sample is being encoded by the encoder of the weighted autoencoder of the weighted autoencoder and pixel convolutional neural network, a hidden layer feature;
• and
• input the test encoding feature into a weighted pixel convolution neural network of the weighted autoencoder and pixel convolutional neural network to output a test result, the test result being either no defect in the test sample or at least one defect in the test sample.

A non-transitory storage medium storing a set of commands, when the commands being executed by at least one processor of an electronic device, causing the at least one processor to:

• obtain a trained weighting generated when an autoencoder and pixel convolutional neural network is being trained with a plurality of defect-free training samples;
• input the trained weighting into the autoencoder and pixel convolutional neural network to obtain a test encoding feature;
• the test encoding feature is obtained by encoding test samples using a weighted autoencoder of the weighted autoencoder and pixel convolutional neural network;
• the test encoding feature is a feature generated when the test sample is being encoded by the encoder of the weighted autoencoder of the weighted autoencoder and pixel convolutional neural network, a hidden layer feature;
• and
• input the test encoding feature into a weighted pixel convolution neural network of the weighted autoencoder and pixel convolutional neural network to output a test result, the test result being either no defect in the test sample or at least one defect in the test sample.