Defect detection method, device and system

A defect detection apparatus, comprising:

• an obtaining module, configured to obtain an average grayscale value of an image of an object to be detected;
• a construction module, configured to construct a mapping table, wherein elements of the mapping table comprise a mapped value corresponding to each grayscale value within a grayscale value range of the image, a mapped value corresponding to a grayscale value greater than or equal to a reference value is a first value, a mapped value corresponding to a grayscale value less than the reference value is a second value, and the reference value is an absolute value of a difference between the average grayscale value and a preset grayscale value;
• a search module, configured to search for a mapped value corresponding to a grayscale value of each pixel in the image from the mapping table;
• a segmentation module, configured to segment the image to obtain at least one suspicious defect sub-image based on the mapped value corresponding to the grayscale value of each pixel, wherein a mapped value corresponding to a grayscale value of each pixel in each suspicious defect sub-image is the first value;
• and
• an input module, configured to input the at least one suspicious defect sub-image into a machine learning model to obtain a defect detection result.

A non-transitory computer-readable storage medium storing computer program instructions, wherein when executed by a processor, the computer program causes the processor to perform a defect detection method that comprises:

• obtaining an average grayscale value of an image of an object to be detected;
• constructing a mapping table, wherein elements of the mapping table comprise a mapped value corresponding to each grayscale value within a grayscale value range of the image, a mapped value corresponding to a grayscale value greater than or equal to a reference value is a first value, a mapped value corresponding to a grayscale value less than the reference value is a second value, and the reference value is an absolute value of a difference between the average grayscale value and a preset grayscale value;
• searching for a mapped value corresponding to a grayscale value of each pixel in the image from the mapping table;
• segmenting the image to obtain at least one suspicious defect sub-image based on the mapped value corresponding to the grayscale value of each pixel, wherein a mapped value corresponding to a grayscale value of each pixel in each suspicious defect sub-image is the first value;
• and
• inputting the at least one suspicious defect sub-image into a machine learning model to obtain a defect detection result.