Defect inspection device, defect inspection method, and prediction model generation method

A defect inspection method, comprising:

• a first step of acquiring an inspection image that is a captured image of an object to be inspected by an inspection image acquisition unit of a computer;
• and
• a second step of applying the inspection image acquired by the inspection image acquisition unit to a prediction model trained by using training data, and performing prediction whether the object to be inspected is a normal product, and prediction of a defect type in a case where the object to be inspected is a defective product by a prediction unit of the computer,
• wherein the training data is configured by assigning a normal product ground truth label that does not include a label indicating a possibility of correspondence to a defective product and includes normal label indicating a possibility of correspondence to a normal product to a learning image of the normal product, and by assigning a defective product ground truth label that does not include a normal label indicating a possibility of correspondence to the normal product and includes a plurality of defective type labels indicating a possibility of correspondence to a plurality of defect types, and a weight for each of the defect type labels to a learning image of a defective product.

A prediction model generation method, comprising:

• a first step of inputting training data to which a ground truth label is assigned by a training data input unit of a computer;
• and
• a second step of performing machine learning processing by a prediction model generation unit of the computer by using the training data input by the training data input unit to generate a prediction model that outputs a prediction result as to whether an object to be inspected is a normal product, and a prediction result of a defect type in a case where the object to be inspected is a defective product when an inspection image that is a captured image of the object to be inspected is input,
• wherein the training data is configured by assigning a normal product ground truth label that does not include a label indicating a possibility of correspondence to a defective product and includes only a normal label indicating a possibility of correspondence to a normal product to a learning image of the normal product, and by assigning a defective product ground truth label that does not include a normal label indicating a possibility of correspondence to the normal product and includes a plurality of defective type labels indicating a possibility of correspondence to a plurality of defect types, and a weight for each of the defect type labels to a learning image of a defective product.