Method and apparatus for classifying images of solar battery cells

An apparatus, comprising:

• a memory for storing an EL image of a solar battery cell being inspected;
• and
• a control unit for processing the stored EL image of the solar battery cell being inspected and controlling to analyze whether the solar battery cell being inspected is defective,
• wherein the control unit classifies an EL image of a solar battery cell being inspected, according to whether a black spot occupies a certain percentage or more of the EL image of the solar battery cell being inspected, namely, primarily classifies the EL image of the solar battery cell being inspected into a first type in which a black spot occupies a certain percentage or more, or a second type in which a black spot occupies less than the certain percentage, and secondarily classifies the type of defect of the solar battery cell being inspected, based on the EL image of the solar battery cell being inspected, by using a pre-trained defect classification model, if the EL image of the solar battery cell being inspected is primarily classified into the first type, and
• wherein the defect classification model is a model which is trained according to a machine learning technique by using learning data that respectively includes input data for learning EL images and result data for the type of defect of the solar battery cell of the learning EL images.

An apparatus, comprising:

• a communication unit for receiving an EL image of a solar battery cell being inspected;
• and
• a control unit for processing the received EL image of the solar battery cell being inspected and controlling to analyze whether the solar battery cell being inspected is defective,
• wherein the control unit classifies an EL image of a solar battery cell being inspected, according to whether a black spot occupies a certain percentage or more of the EL image of the solar battery cell being inspected, namely, primarily classifies the EL image of the solar battery cell being inspected into a first type in which a black spot occupies a certain percentage or more, or a second type in which a black spot occupies less than the certain percentage, and secondarily classifies the type of defect of the solar battery cell being inspected, based on the EL image of the solar battery cell being inspected, by using a pre-trained defect classification model, if the EL image of the solar battery cell being inspected is primarily classified into the first type, and
• wherein the defect classification model is a model which is trained according to a machine learning technique by using learning data that respectively includes input data for learning EL images and result data for the type of defect of the solar battery cell of the learning EL images.