Invention Grant
- Patent Title: Method for object detection using hierarchical deep learning
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Application No.: US17073041Application Date: 2020-10-16
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Publication No.: US11367189B2Publication Date: 2022-06-21
- Inventor: Daniel Clymer , Jonathan Cagan , Philip LeDuc
- Applicant: Carnegie Mellon University
- Applicant Address: US PA Pittsburgh
- Assignee: Carnegie Mellon University
- Current Assignee: Carnegie Mellon University
- Current Assignee Address: US PA Pittsburgh
- Agency: KDB Firm PLLC
- Main IPC: G06T7/00
- IPC: G06T7/00 ; G06K9/62 ; G06V20/69

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.
Public/Granted literature
- US20210406602A1 METHOD FOR OBJECT DETECTION USING HIERARCHICAL DEEP LEARNING Public/Granted day:2021-12-30
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