US Patent Issued to Carnegie Mellon University on Dec. 9 for "Method for object detection using hierarchical deep learning" (Pennsylvania Inventors)

ALEXANDRIA, Va., Dec. 9 -- United States Patent no. 12,494,071, issued on Dec. 9, was assigned to Carnegie Mellon University (Pittsburgh).

"Method for object detection using hierarchical deep learning" was invented by Jonathan Cagan (Pittsburgh), Philip LeDuc (Pittsburgh) and Daniel Clymer (Pittsburgh).

According to the abstract* released by the U.S. Patent & Trademark Office: "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 pat...