Machine-learning based defect identification in images of computing devices

A computer program product comprising a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device:

• to obtain an image of at least a portion of a computing device;
• to process the image of the portion of the computing device to generate two or more segments thereof for inclusion in a first data structure;
• to determine a set of one or more labels to be provided as input to a first machine learning model, the one or more labels being associated with one or more designated defect types to be detected;
• to apply the determined set of one or more labels and at least a portion of the first data structure to the first machine learning model to determine, for inclusion in a second data structure, probabilities of the one or more designated defect types being present in each of the two or more segments of the image of the portion of the computing device;
• to determine, based at least in part on at least a portion of the second data structure, one or more input prompts for a second machine learning model;
• to apply the determined one or more input prompts and at least a portion of the second data structure to the second machine learning model to generate answers to the determined one or more input prompts;
• and
• to identify, based at least in part on the generated answers to the determined one or more input prompts, whether the portion of the computing device includes any defects of the one or more designated defect types.

A method comprising:

• obtaining an image of at least a portion of a computing device;
• processing the image of the portion of the computing device to generate two or more segments thereof for inclusion in a first data structure;
• determining a set of one or more labels to be provided as input to a first machine learning model, the one or more labels being associated with one or more designated defect types to be detected;
• applying the determined set of one or more labels and at least a portion of the first data structure to the first machine learning model to determine, for inclusion in a second data structure, probabilities of the one or more designated defect types being present in each of the two or more segments of the image of the portion of the computing device;
• determining, based at least in part on at least a portion of the second data structure, one or more input prompts for a second machine learning model;
• applying the determined one or more input prompts and at least a portion of the second data structure to the second machine learning model to generate answers to the determined one or more input prompts;
• and
• identifying, based at least in part on the generated answers to the determined one or more input prompts, whether the portion of the computing device includes any defects of the one or more designated defect types;
• wherein the method is performed by at least one processing device comprising a processor coupled to a memory.