Invention Publication
- Patent Title: METHOD FOR OBJECT DETECTION USING HIERARCHICAL DEEP LEARNING
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Application No.: US18391820Application Date: 2023-12-21
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Publication No.: US20240169748A1Publication Date: 2024-05-23
- Inventor: JONATHAN CAGAN , PHILIP LeDUC , DANIEL CLYMER
- Applicant: CARNEGIE MELLON UNIVERSITY
- Applicant Address: US PA Pittsburgh
- Assignee: CARNEGIE MELLON UNIVERSITY
- Current Assignee: CARNEGIE MELLON UNIVERSITY
- Current Assignee Address: US PA Pittsburgh
- Main IPC: G06V20/69
- IPC: G06V20/69 ; G06F18/10 ; G06F18/2135 ; G06F18/214 ; G06F18/241 ; G06F18/2415 ; G06T7/00

Abstract:
A hierarchical deep-learning object detection framework provides a method for identifying objects of interest in high-resolution, high pixel count images, wherein the objects of interest comprise a relatively a small pixel count when compared to the overall image. The method uses first deep-learning model to analyze the high pixel count images, in whole or as a patchwork, at a lower resolution to identify objects, and a second deep-learning model to analyze the objects at a higher resolution to classify the objects.
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