Carnegie Mellon University

Daniel Clymer

Philip LeDuc

Jonathan Cagan

Janet Catov

Liron Pantanowitz

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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Pittsburgh, PA, United States of America

Daniel Clymer