Systems and methods for part identification and assessment using multiple images

An object identification system, comprising:

• one or more processors;
• and
• one or more memory devices having stored thereon instructions that when executed by the one or more processors cause the one or more processors to:
• train an object identification model, including:
• collecting a plurality of training images for each of a plurality of objects;
• labeling each of the plurality of training images with a corresponding one of a plurality of object identifiers;
• and
• training a neural network with the plurality of labeled training images;
• receive at least two target images of a target object;
• feed each of the at least two target images into the trained object identification model;
• receive, from the trained object identification model, for each of the at least two target images, a probability that the target object corresponds to each of the plurality of object identifiers;
• compute a similarity value between the at least two target images;
• combine, for each of the plurality of object identifiers, the corresponding probabilities for the at least two target images in proportion to the similarity value;
• and
• identify the target object as the object identifier having the greatest combined probability.

One or more non-transitory computer-readable media storing computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:

• training an object identification model, including:
• collecting a plurality of training images for each of a plurality of objects;
• labeling each of the plurality of training images with a corresponding one of a plurality of object identifiers;
• and
• training a neural network with the plurality of labeled training images;
• receiving at least two target images of a target object;
• feeding each of the at least two target images into the trained object identification model;
• receiving, from the trained object identification model, for each of the at least two target images, a target object identifier corresponding to the target object and a probability that the target object identifier corresponds to the target object;
• computing a similarity value between the at least two target images;
• combining the probabilities for the at least two target images in proportion to the similarity value;
• and
• identifying the target object as the target object identifier.