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Method for object detection using shallow neural networks

  • US 10,789,527 B1
  • Filed: 11/13/2019
  • Issued: 09/29/2020
  • Est. Priority Date: 03/31/2019
  • Status: Active Grant
First Claim
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1. A method for object detection, the method comprises:

  • receiving an input image by an input of an object detector;

    wherein the object detector comprises multiple branchesgenerating at least one downscaled version of the input image;

    feeding the input image to a first branch of the multiple branches;

    feeding each one of the at least one downscale version of the input image to a unique branch of the multiple branches, one downscale version of the image per branch;

    calculating, by the multiple branches, candidate bounding boxes that are indicative of candidate objects that appear in the input image and each one of the at least one downscaled version of the input image;

    selecting bounding boxes out of the candidate bounding boxes, by a selection unit that followed the multiple branches;

    wherein the multiple branches comprise multiple shallow neural networks that are followed by multiple region units;

    wherein each branch comprises a shallow neural network and a region unit;

    wherein the multiple shallow neural networks are multiple instances of a single trained shallow neural network; and

    wherein the single trained shallow neural network is trained to detect objects having a size that is within a predefined size range and to ignore objects having a size that is outside the predefined size range.

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