Image defect detection method, electronic device and readable 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:
• acquire an image to be detected, a defective image, and a non-defective image;
• divide the image to be detected into a plurality of detecting areas, generate a detection accuracy value for each of the plurality of detecting areas based on the defective image and the non-defective image, and obtain a plurality of detection accuracy values;
• construct a plurality of learners;
• select an autoencoder for each of the plurality of detection accuracy values from the plurality of learners;
• obtain a multi-scale autoencoder model corresponding to each of the plurality of detecting areas by training the corresponding autoencoder based on the non-defective image;
• divide each of the plurality of detecting areas into a plurality of detecting blocks, and obtain a plurality of reconstructed image blocks by inputting each of the plurality of detecting blocks into the corresponding multi-scale autoencoder model;
• calculate a reconstruction error value between each of the plurality of reconstructed image blocks and the corresponding detecting block;
• and
• generate a detection result of a product contained in the image to be detected based on the reconstruction error value corresponding to each detecting block.

A non-transitory storage medium having instructions stored thereon, when the instructions are executed by a processor of an electronic device, the processor is caused to perform an image defect detection method, wherein the method comprises:

• acquiring an image to be detected, a defective image, and a non-defective image;
• dividing the image to be detected into a plurality of detecting areas, generating a detection accuracy value for each of the plurality of detecting areas based on the defective image and the non-defective image, and obtaining a plurality of detection accuracy values;
• constructing a plurality of learners;
• selecting an autoencoder for each of the plurality of detection accuracy values from the plurality of learners;
• obtaining a multi-scale autoencoder model corresponding to each of the plurality of detecting areas by training the corresponding autoencoder based on the non-defective image;
• dividing each of the plurality of detecting areas into a plurality of detecting blocks, and obtaining a plurality of reconstructed image blocks by inputting each of the plurality of detecting blocks into the corresponding multi-scale autoencoder model;
• calculating a reconstruction error value between each of the plurality of reconstructed image blocks and the corresponding detecting block;
• generating a detection result of a product contained in the image to be detected based on the reconstruction error value corresponding to each detecting block.