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 learned model generation method comprising:

• using a teacher image including
• a defect map that is an image indicating a distribution of a defect portion of a surface of steel and having an equal image size, and
• presence/absence of periodic defects assigned in advance to the defect map;
• and
• generating a learned model by machine learning, the learned model for which:
• an input value is a defect map that is an image indicating a distribution of a defect portion of a surface of steel and having an image size of the equal image size;
• and
• an output value is a value concerning presence/absence of periodic defects in the defect map.

A learned model comprising:

• an input layer configured to be input, as a determination image, a defect map that is an image indicating a distribution of a defect portion of a surface of steel and having an equal image size;
• an output layer configured to output a value concerning presence/absence of periodic defects for the determination image;
• and
• an intermediate layer for which parameters have been learned by using a teacher image, the teacher image for which:
• an input is a defect map having the equal image size as an image size of the determination image;
• and
• an output is a value concerning the presence/absence of periodic defects for the defect map, wherein
• the learned model causes a computer to function to:
• input a determination image of the equal image size into the input layer;
• calculate in the intermediate layer;
• and
• output the value concerning the presence/absence of periodic defects from the output layer.

A surface defect inspection method comprising:

• acquiring imaged data for which a surface of steel has been captured;
• creating a defect map 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 an equal 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.

A pass/fail determination method comprising:

• acquiring imaged data for which a surface of steel has been captured;
• creating a defect map 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 an equal 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.

A grade determination method comprising:

• acquiring imaged data for which a surface of steel has been captured;
• creating a defect map 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 an equal image size;
• 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;
• calculating a defect mixing rate of the determination image based on the defect portion;
• and
• determining a grade of the steel corresponding to the determination image, based on the calculated defect mixing rate and a determination result of the presence/absence of periodic defects for the determination image.

A grade determination method comprising:

• acquiring imaged data for which a surface of steel has been captured;
• creating a defect map 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 an equal image size;
• 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;
• calculating a defect mixing rate of the determination image based on the defect portion;
• and
• determining a grade of the steel corresponding to the determination image, based on the calculated defect mixing rate and a determination result of pass/fail for the steel.

A surface defect determination program that causes a computer to perform processing including:

• acquiring imaged data for which a surface of steel has been captured;
• creating a defect map 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 an equal 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.

A pass/fail determination program that causes a computer to perform processing including:

• acquiring imaged data for which a surface of steel has been captured;
• creating a defect map 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 an equal 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.

A determination system comprising:

• a defect map creation unit configured to create a defect map 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 an equal 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 equal 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.