Method and apparatus for detecting defect based on phased pass/fail determination

A method of detecting a defect of a product, comprising:

• acquiring a data set including a product image of the product;
• inputting the data set into a first determination model trained to perform a first pass/fail determination of the product;
• extracting, from the first determination model, a first determination result;
• determining whether the first determination result indicates that the first determination model failed to perform the first pass/fail determination;
• in response to the first determination result indicating that the first determination model failed to perform the first pass/fail determination, inputting the data set to a second determination model trained to perform a second pass/fail determination;
• extracting, from the second determination model, a second determination result of the second pass/fail determination;
• and
• determining, based on the second determination result, whether the product has a defect,
• wherein inputting the data set into the second determination model comprises inputting the data set to a deep learning-based object detection model or a deep learning-based segmentation model trained to perform the second pass/fail determination based on a detection region in the product image included in the acquired data set, and
• wherein inputting the data set into the second determination model comprises inputting the data set to the second determination model trained to:
• extract the detection region from the product image included in the data set;
• extract a detection pattern from the detection region;
• and
• perform, based on the extracted detection pattern, the second pass/fail determination.