Nuanced target recognition

Patent metadata

Field	Value
Publication number	US-2026045066-A1
Application number	US-202519291018-A
Country	US
Kind code	A1
Filing date	Aug 5, 2025
Priority date	Aug 6, 2024
Publication date	Feb 12, 2026
Grant date	—

Abstract

A system for nuanced target recognition, comprising one or more processors coupled with memory, the one or more processors may be configured to detect, using a second model, an object based on a sequence of images, determine, using the second model, a class of the object for one or more of the images of the sequence of images, based on the images and an output of a first model, wherein the output comprises class definitions associated with a plurality of objects, generate a classification of the object based on the determined classes for the one or more images, and present the object and the classification on a display coupled with the one or more processors.

First claim

What is claimed is:

1 . A system for nuanced target recognition, comprising one or more processors coupled with memory, the one or more processors configured to:
detect, using a second model, an object based on a sequence of images; determine, using the second model, a class of the object for one or more of the images of the sequence of images, based on the images and an output of a first model, wherein the output comprises class definitions associated with a plurality of objects; generate a classification of the object based on the determined classes for the one or more images; and present the object and the classification on a display coupled with the one or more processors.

2 . The system of claim 1 , wherein the one or more processors are configured to:
identify, for each of the one or more images of the sequence of images, a bounding box around the object; determine the class for each object of the one or more images based on each respective bounding box; determine, using the second model and the class for each object of the one or more images, the classification of the objects from a plurality of classes.

3 . The system of claim 2 , wherein the one or more processors are configured to:
link, using a third model, the bounding boxes of each of the one or more images to generate a tubelet; determine that a first image of the one or more images having a first class is sequential to a second image of the one or more images having a second class, wherein the second class is different than the first class; and update the object in the first image to have the second class.

4 . The system of claim 1 , wherein the one or more processors are configured to:
receive an input describing a second object; generate, using the first model, an embedding of the input; determine, using the first model, a cosine similarity of the input based on the embedding; and determine, using the first model, a second class definition to store with the plurality of class definitions.

5 . The system of claim 4 , wherein the input includes at least one of a text description of the second object or an image of the second object.

6 . The system of claim 4 , wherein the one or more processors are configured to:
determine the cosine similarity between the classes; provide an indication of the cosine similarity via a display device; and receive an update to the input.

7 . The system of claim 1 , wherein the one or more processors are configured to generate a background from the sequence of images via a mosaic or image stitching algorithm.

8 . The system of claim 7 , wherein the one or more processors are configured to overlay the object on the background based on an aggregation of each detection and classification.

9 . The system of claim 1 wherein the sequence of images includes one or more of electro-optical images, infrared images, visible light images, ultraviolet light images, sonar images, radar images, or synthetic aperture radar images.

10 . The system of claim 1 , comprising an image capture device to capture the sequence of images.

11 . The system of claim 1 , comprising a drone configured to couple with the one or more processors.

12 . The system of claim 1 , wherein the one or more processors are configured to:
identify, using the second model, a second object; determine, based on a plurality of classes and the second model, that a second class of the second object is not in the determined classes; and present, via the display, an indication of the second class.

13 . A method for nuanced target recognition, comprising:
receiving a sequence of images; receiving a natural language description of a desired object; analyzing the natural language description and generating a feedback interface including at least one initial classification and at least one adjustment option; receiving adjustments in response to the feedback interface; refining initial classification based on the adjustments; and providing the refined classification for object detection within the sequence of images.

14 . The method of claim 13 , comprising:
identifying for each of the one or more images of the sequence of images, a bounding box around the object.

15 . The method of claim 14 , wherein analyzing user input includes embedding the input and determining a cosine similarity of the user input based on the embedding.

16 . The method of claim 15 , wherein the feedback interface includes a ranking of most similar and least similar classes.

17 . The method of claim 16 , further comprising determining that the cosine similarity is above a threshold similarity.

18 . A non-transitory computer-readable medium having instructions embodied thereon, the instructions to cause one or more processors to:
identify an object based on a sequence of images; identify, for each of the one or more images of the sequence of images, a bounding box around the object, generate a tubelet of multiple ones of the sequence of images, overlay a Gaussian Kernel Density Estimate proportional to the dimensions of each bounding box, aggregate the ones of the sequence of images including the object based on the overlay to generate a heat map representation of overlapping frames.

19 . The non-transitory computer-readable medium of claim 18 , wherein the instructions cause the one or more processors to:
determine a class for each of the one or more images based on each respective bounding box; determine a classification of the object from a plurality of classes.

20 . The non-transitory computer-readable medium of claim 19 , wherein the instructions cause the one or more processors to:
determine that a first image of the one or more images having a first class is sequential to a second image of the one or more images having a second class, wherein the second class is different than the first class; and update the first image to have the second class.

Assignees

• Charles Stark Draper Laboratory Inc

Inventors

• Palladino Anthony

Classifications

• G06V10/82

  using neural networks · CPC title

• G06V10/764 Primary

  using classification, e.g. of video objects · CPC title