Learned model generation method, learned model, surface defect inspection method, steel manufacturing method, pass/fail determination method, grade determination method, surface defect determination program, pass/fail determination program, determination system, and steel manufacturing equipment

A method comprising:

• acquiring imaged data for which a surface of steel has been captured;
• creating a defect map that is an image indicating a distribution of a defect portion based on the imaged data;
• creating a determination image for each defect map by converting an image size of the defect map into a predetermined image size;
• and
• inputting the determination image into a learned model, outputting a value concerning presence/absence of periodic defects, and determining a pass/fail of the steel based on the value concerning the presence/absence of periodic defects, wherein
• the learned model is trained using
• a teacher image as an input, the teacher image (i) having the predetermined image size and (ii) being a defect map indicating a distribution of defect portions on the surface of the steel, and
• a value concerning presence or absence of periodic defects in the teacher image as an output, and
• the predetermined image size has a correct answer rate associated with the learned model that falls within an allowable range.

A non-transitory computer readable medium storing a program that, when executed, causes a computer to perform processing including:

• acquiring imaged data for which a surface of steel has been captured;
• creating a defect map that is an image indicating a distribution of a defect portion based on the imaged data;
• creating a determination image for each defect map by converting an image size of the defect map into a predetermined image size;
• and
• inputting the determination image into a learned model, outputting a value concerning presence/absence of periodic defects, and determining the presence/absence of periodic defects for the determination image based on the value concerning the presence/absence of periodic defects, wherein
• the learned model is trained using
• a teacher image as an input, the teacher image (i) having the predetermined image size and (ii) being a defect map indicating a distribution of defect portions on the surface of the steel, and
• a value concerning presence or absence of periodic defects in the teacher image as an output, and
• the predetermined image size has a correct answer rate associated with the learned model that falls within an allowable range.

A non-transitory computer readable medium storing a program that, when executed, causes a computer to perform processing including:

• acquiring imaged data for which a surface of steel has been captured;
• creating a defect map that is an image indicating a distribution of a defect portion based on the imaged data;
• creating a determination image for each defect map by converting an image size of the defect map into a predetermined image size;
• and
• inputting the determination image into a learned model, outputting a value concerning presence/absence of periodic defects, and determining a pass/fail of the steel based on the value concerning the presence/absence of periodic defects, wherein
• the learned model is trained using
• a teacher image as an input, the teacher image (i) having the predetermined image size and (ii) being a defect map indicating a distribution of defect portions on the surface of the steel, and
• a value concerning presence or absence of periodic defects in the teacher image as an output, and
• the predetermined image size has a correct answer rate associated with the learned model that falls within an allowable range.

A determination system comprising:

• a defect map creation unit configured to create a defect map that is an image indicating a distribution of a defect portion based on imaged data for which a surface of steel has been captured;
• a size conversion unit configured to convert an image size of the defect map into a predetermined image size to create a determination image for each defect map;
• a periodic-defect determination unit configured to input the determination image into a machine-learned learned model and output a value concerning presence/absence of periodic defects;
• and
• a determination unit configured to determine the presence/absence of periodic defects for the determination image and/or determine a pass/fail of the steel, based on the value concerning the presence/absence of periodic defects, wherein
• the learned model has been subjected to machine learning by using a teacher image of the predetermined image size as an image size of the determination image such that the value concerning the presence/absence of periodic defects is output when the determination image is input,
• the learned model is trained using
• a teacher image as an input, the teacher image (i) having the predetermined image size and (ii) being a defect map indicating a distribution of defect portions on the surface of the steel, and
• a value concerning presence or absence of periodic defects in the teacher image as an output, and
• the predetermined image size has a correct answer rate associated with the learned model that falls within an allowable range.