Method and system for generating a detector for process monitoring

A system for detecting objects, the system comprising:

• at least one camera configured to capture a plurality of images;
• and
• a controller configured to:
• obtain a first training dataset including a first image sequence having a first set of object tags identifying at least one first object class in a corresponding image of the first image sequence;
• obtain a first set of ground truth tags based on a ground truth timeline identifying when the at least one first object class appeared in the first image sequence;
• discard images from the first training dataset by either identifying object tags by class from the first set of object tags without a corresponding ground truth tag from the first set of ground truth tags or identifying object tags by class from the first set of ground truth tags without a corresponding object tag from the first set of object tags to generate a first verified training dataset;
• train a first parts-level detector based on the first verified training dataset;
• generate an additional verified training dataset based on an additional training dataset including an additional image sequence having an additional set of object tags identifying at least one additional object class, and an additional set of ground truth tags based on an additional ground truth timeline identifying when the at least one additional object class appeared in the additional image sequence;
• train an additional parts-level detector based on the additional verified training dataset;
• and
• train a unified detector by utilizing the first parts-level detector on the additional verified training dataset and utilizing the additional parts-level detector on the first verified training dataset.

A non-transitory computer readable medium embodying programmed instructions which, when executed by a processor, are operable for performing a method comprising:

• obtaining a first training dataset including a first image sequence having a first set of object tags identifying at least one first object class in a corresponding image of the first image sequence;
• obtaining a first set of ground truth tags based on a ground truth timeline identifying when the at least one first object class appeared in the first image sequence;
• discarding images from the first training dataset by either identifying object tags by class from the first set of object tags without a corresponding ground truth tag from the first set of ground truth tags or identifying object tags by class from the first set of ground truth tags without a corresponding object tag from the first set of object tags to generate a first verified training dataset;
• training a first parts-level detector based on the first verified training dataset;
• generating an additional verified training dataset based on an additional training dataset including an additional image sequence having an additional set of object tags identifying at least one additional object class, and an additional set of ground truth tags based on an additional ground truth timeline identifying when the at least one additional object class appeared in the additional image sequence;
• training an additional parts-level detector based on the additional verified training dataset;
• and
• training a unified detector by utilizing the first parts-level detector on the additional verified training dataset and utilizing the additional parts-level detector on the first verified training dataset.