Method for object detection using shallow neural networks
First Claim
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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Abstract
A method that may include feeding an input image and downscaled versions of the input image to multiple branches of an object detector calculating, by the multiple branches, candidate bounding boxes; and selecting bounding boxes. The multiple branches comprise multiple shallow neural networks that are followed by multiple region units. Each branch includes a shallow neural network and a region unit. The multiple shallow neural networks are multiple instances of a single trained shallow neural network. 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.
358 Citations
30 Claims
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1. A method for object detection, the method comprises:
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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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A non-transitory computer readable medium for detecting an object by an object detector, wherein the non-transitory computer readable medium stores instructions for:
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receiving an input image by an input of the object detector;
wherein the object detector comprises multiple branches;generating 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 follows 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. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20)
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21. An object detection system that comprises an input, a downscaling unit, multiple branches, and a selection unit;
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wherein the input is configured to receive an input image; wherein the downscaling unit is configured to generate at least one downscaled version of the input image; wherein the multiple branches are configured to receive the input image and the at least one downscaled version of the input image, one image per branch; wherein the multiple branches are configured to calculate 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; wherein the selection unit is configured to select bounding boxes out of the candidate bounding boxes; 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. - View Dependent Claims (22, 23, 24, 25, 26, 27, 28, 29, 30)
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Specification