Surface defect detection model training method, and surface defect detection method and system

A method for training a surface defect detection model, comprising:

• acquiring a first image and a second image, wherein the first image corresponds to an image of a product;
• and
• inputting the first image and the second image into a deep neural network-based surface defect detection model and training the deep neural network-based surface defect detection model to obtain the trained surface defect detection model;
• wherein the loss function corresponding to a training process is expressed as:
• loss
• i
• =
• 1
• W
• ×
• H
• ⁢
• (
• ∑
• i
• =
• 1
• ,
• j
• =
• 1
• W
• ,
• H
• ⁢
• (
• 1
• -
• Y
• i
• )
• ⁢
• (
• 1
• -
• exp
• ⁡
• (
• -
• F
• i
• ,
• j
• )
• )
• +
• ∑
• i
• =
• 1
• ,
• j
• =
• 1
• W
• ,
• H
• ⁢
• (
• Y
• i
• )
• ⁢
• (
• F
• i
• ,
• j
• )
• )
• ;
• wherein lossi denotes the loss function, W denotes a width of the distance matrix, H denotes a height of the distance matrix, Yi denotes a tag of an i-th image, Yi=1 represents a first image, Yi=0 represents an image not satisfying a predetermined threshold requirement, Fi, j denotes a value at a position (i, j) in the distance matrix.