Reinforcement Learning Object Detection Reducing Computational Redundancy
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Solution Overview
Problem
Existing computer vision systems with reinforcement learning for object detection suffer from duplicative computational efforts when the reinforcement learning agent fails to recognize previously detected objects, leading to inefficiencies in detecting and classifying objects in images.
Innovation Solution
A computer vision system and method that utilizes a reinforcement learning agent to detect objects and determine bounding boxes without requiring a classifier to eliminate false-positives, allowing the agent to learn object classes efficiently and provide accurate object detection without duplicative search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the reinforcement learning agent repeatedly executes the detection algorithm over different portions of the image to detect multiple objects, then multi-object detection capability is improved, but duplicative computational efforts increase when previously detected objects are not recognized
Solution Approach 1:
The system maintains a record of previously detected objects and their bounding boxes before executing the detection algorithm again. By checking against this pre-established record, the system prevents duplicative computational efforts on already detected objects while maintaining multi-object detection capability.
Solution Approach 2:
The system implements a feedback mechanism where the reinforcement learning agent receives information about previously detected objects and adjusts its detection process accordingly. This feedback loop allows the agent to avoid re-detecting the same objects, thereby improving computational efficiency without sacrificing detection accuracy.
2Reliability
If the reinforcement learning agent processes the entire image to ensure comprehensive object detection, then detection coverage is improved, but computational power is wasted on areas that have already been detected
Solution Approach 1:
The system segments the image processing task by dividing it into previously detected regions and undetected regions. By maintaining records of detected objects and their bounding boxes, the system can focus computational power only on undetected portions of the image, reducing overall computational power consumption while maintaining comprehensive detection coverage.
Solution Approach 2:
Instead of processing the entire image repeatedly, the system performs partial processing by focusing only on regions that have not been detected yet. This partial action approach maintains reliable detection coverage while significantly reducing computational power consumption compared to full-image re-processing.
3Measurement precision
If the system uses a classifier to eliminate false-positives for single and multiple target object classes, then detection accuracy is improved, but the system complexity increases and duplicative search results occur
Solution Approach 1:
The reinforcement learning agent performs self-service by inherently learning to distinguish between detected and undetected objects through its training process. The agent develops the capability to avoid duplicative detections and false-positives through its own learned policies, eliminating the need for separate classifier components and reducing overall system complexity while maintaining detection accuracy.
Data Source
AI summary
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.