Computer Vision Systems and Methods for Object Detection with Reinforcement Learning
Computer vision systems and methods for object detection with reinforcement learning are provided. The system includes a reinforcement learning agent configured to detect an object pertaining to a target object class and a plurality of objects pertaining to different target object classes, such that the reinforcement learning agent determines a bounding box for each of the detected of objects. The system first sets parameters of the reinforcement learning agent. The system then detects an object and/or objects in an image based on the set parameters. Finally, the system determines a bounding box and/or bounding boxes for each of the detected objects.
1 . A computer vision system for object detection with reinforcement learning, comprising:
a memory storing at least one image; and
a processor in communication with the memory, the processor:
setting a plurality of reinforcement learning agent parameters;
retrieving the at least one image from memory;
detecting a target object in the at least one image based on the reinforcement learning agent parameters;
determining a bounding box for the detected target object;
displaying the bounding box on the image; and
performing reinforcement learning a portion of the image appearing within the bounding box.
2 . The computer vision system of claim 1 , wherein the bounding box magnifies the portion of the image.
3 . The computer vision system of claim 2 , wherein the bounding box is shifted within the image to magnify the portion of the image.
4 . The computer vision system of claim 1 , wherein the bounding box is centered within the image.
5 . The computer vision system of claim 1 , wherein the bounding box magnifies all of the image.
6 . The computer vision system of claim 1 , wherein the bounding box is split into a first bounding box and into a second bounding box when the processor detects more than one object in a region of the image.
7 . The computer vision system of claim 6 , wherein the first bounding box is processed if the processor determines that the first bounding and the second overlap.
8 . The computer vision system of claim 1 , wherein the processor selects an aspect ratio and a size of a region of the image.
9 . The computer vision system of claim 1 , wherein the processor opposes movement of the bounding box in response to a triggering action.
10 . The computer vision system of claim 1 , wherein the processor is trained with one or more of single class object and multiple class object categories to determine the bounding box.
11 . The computer vision system of claim 1 , wherein the processor learns to initiate a trigger action for each class category if multiple class objects are detected in the image.
12 . A computer vision method for object detection with reinforcement learning, comprising the steps of:
setting by a processor a plurality of reinforcement learning agent parameters;
retrieving by the processor at least one image from a memory;
detecting by the processor a target object in the at least one image based on the reinforcement learning agent parameters;
determining by the processor a bounding box for the detected target object;
displaying the bounding box on the image; and
performing by the processor reinforcement learning a portion of the image appearing within the bounding box.
13 . The computer vision method of claim 12 , further comprising magnifying the portion of the image.
14 . The computer vision method of claim 13 , further comprising shifting the bounding box within the image to magnify the portion of the image.
15 . The computer vision method of claim 12 , further comprising centering the bounding box within the image.
16 . The computer vision method of claim 12 , further comprising magnifying all of the image using the bounding box
17 . The computer vision method of claim 12 , further comprising splitting the bounding box into a first bounding box and into a second bounding box when more than one object is detected in a region of the image.
18 . The computer vision method of claim 17 , further comprising processing the first bounding box if the first and second bounding boxes overlap.
19 . The computer vision method of claim 12 , further comprising selecting by the processor an aspect ratio and a size of a region of the image.
20 . The computer vision method of claim 12 , further comprising opposing by the processor movement of the bounding box in response to a triggering action.
21 . The computer vision method of claim 12 , further comprising training the processor with one or more of single class object and multiple class object categories to determine the bounding box.
22 . The computer vision method of claim 12 , further comprising learning by the processor to initiate a trigger action for each class category if multiple class objects are detected in the image.