US 12,254,612 B1
Method for constructing defect detection model, method for detecting defect and related apparatus
Xian Tao, Beijing (CN); Shichen Qu, Beijing (CN); and Zhen Qu, Beijing (CN)
Assigned to INSTITUTE OF AUTOMATION CHINESE ACADEMY OF SCIENCES, Beijing (CN)
Filed by INSTITUTE OF AUTOMATION CHINESE ACADEMY OF SCIENCES, Beijing (CN)
Filed on Nov. 21, 2024, as Appl. No. 18/955,810.
Claims priority of application No. 202411318189.9 (CN), filed on Sep. 20, 2024.
Int. Cl. G06T 7/00 (2017.01)
CPC G06T 7/0004 (2013.01) [G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30108 (2013.01)] 10 Claims
OG exemplary drawing
 
1. A method for constructing a defect detection model, comprising:
obtaining an initial training image, and adding a simulated anomaly to the initial training image to obtain a simulated anomaly training image;
training a preset defect recognition model according to the initial training image and the simulated anomaly training image to obtain defect position information and mask prompt information;
training a preset defect segmentation model according to the defect position information and the mask prompt information; and
fusing the trained defect recognition model and defect segmentation model to obtain the defect detection model;
wherein the defect recognition model comprises a teacher network branch, a student network branch and an autoencoder network branch; the teacher network branch adopts a fixed network weight, and the student network branch and the autoencoder network branch adopt a trainable network weight; and an output difference between the teacher network branch and the student network branch is the defect position information, and an output difference between the student network branch and the autoencoder network branch is the mask prompt information.