Systems and methods for configuring and using a multi-stage object classification and condition pipeline

A computer-implemented method comprising:

• detecting, via a localization machine learning model, a target object within target image data of a scene;
• classifying, via an object classification machine learning model, the target object to a probable object class of a plurality of distinct object classes based on an input of the target image data of the scene, wherein the plurality of distinct object classes includes an out-of-scope object class and one or more in-scope object classes;
• routing, via the one or more processors, the target image data of the scene to a target object-condition machine learning classification model of a plurality of distinct object-condition machine learning classification models based on a mapping between the plurality of distinct object classes and the plurality of distinct object-condition machine learning classification models;
• classifying, via the target object-condition machine learning classification model, the target object to a probable object-condition class of a plurality of distinct object-condition classes;
• and
• displaying, via a graphical user interface, a representation of the target object in association with the probable object-condition class based on the classifying of the target object via the target object-condition machine learning classification model.

A computer-implemented system comprising:

• one or more processors;
• a memory;
• a computer-readable medium operably coupled to the one or more processors, the computer-readable medium having computer-readable instructions stored thereon that, when executed by the one or more processors, cause a computing device to perform operations comprising:
• detecting, via a localization machine learning model, a target object within target image data of a scene;
• classifying, via an object classification machine learning model, the target object to a probable object class of a plurality of distinct object classes based on an input of the target image data of the scene, wherein the plurality of distinct object classes includes an out-of-scope object class and one or more in-scope object classes;
• routing, via the one or more processors, the target image data of the scene to a target object-condition machine learning classification model of a plurality of distinct object-condition machine learning classification models based on a mapping between the plurality of distinct object classes and the plurality of distinct object-condition machine learning classification models;
• classifying, via the target object-condition machine learning classification model, the target object to a probable object-condition class of a plurality of distinct object-condition classes;
• and
• displaying, via a graphical user interface, a representation of the target object in association with the probable object-condition class based on the classifying of the target object via the target object-condition machine learning classification model.