Stacking color and motion signal to detect tiny objects

A method for detecting an object in an image frame, the method comprising, by one or more processors:

• receiving real-time image data comprising a first image frame and a second image frame;
• processing a first set of image data based on the first image frame;
• processing a second set of image data based on the first image frame and the second image frame;
• inputting the first set of image data and the second set of image data into a network model;
• and
• with the network model, detecting one or more targeted objects from a plurality of potential objects in the first image frame, the one or more targeted objects comprising less than 1/100th pixels of a total number of pixels in the first image frame.

A method for training an object detection system, the method comprising, by one or more processors:

• inputting training data comprising a plurality of potential objects in various scenarios into a network model, the plurality of potential objects comprising one or more targeted objects;
• training the network model based on the training data to detect the one or more targeted objects from among the plurality of potential objects;
• processing a first set of image data based on a first image frame and a second set of image data based on the first image frame and a second image frame;
• inputting the first set of image data and the second set of image data into the network model, the network model configured to detect the one or more targeted objects in the first image frame, the one or more potential objects comprising less than 1/100th pixels of a total number of pixels in the image frame;
• processing at least a portion of the first image frame including at least one of the one or more targeted objects with the network model to detect the one or more targeted objects;
• inputting, via a user interface. a ground truth for any of the one or more targeted objects not detected by the network model;
• determining at least one loss function associated with each of the one or more targeted objects detected;
• and
• re-training the network model based on the at least one loss function.