Reinforcement Learning Target Verification Strategy
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Solution Overview
Problem
Existing automatic target recognition (ATR) systems face challenges in achieving 100% accuracy and efficiency, particularly in processing large amounts of image or video data, such as in underwater mine countermeasure tasks, where human recognition is impractical and automated systems may not guarantee all recognized targets are true mines or objects of interest.
Innovation Solution
The implementation of reinforcement learning to optimize the strategy for automatic target recognition, using a system that receives images, performs ATR, divides candidate targets into subsets based on confidence scores, and interacts with a graphical user interface to verify true targets through user input, employing a Q-learning process to minimize verification time and achieve high accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic target recognition systems are used to process large amounts of image data, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The patent segments the target detection process into multiple stages: initial ATR detection, confidence score calculation, subset division of candidate targets, and selective verification. This segmentation allows the system to quickly process images while applying intensive verification only where needed, thus maintaining both high productivity and measurement precision.
Solution Approach 2:
The patent introduces confidence scores as an intermediary mechanism between automatic detection and manual verification. Targets are ranked by confidence scores, and only those below a threshold are subjected to manual verification. This intermediary layer enables the system to efficiently filter out high-confidence detections while focusing verification resources on uncertain cases, resolving the contradiction between speed and accuracy.
2Measurement precision
If manual verification is performed on all candidate targets, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent applies partial verification by performing manual checking only on a subset of candidate targets—specifically those with confidence scores below a predetermined threshold. High-confidence targets are accepted automatically without manual verification. This partial action approach maintains high detection accuracy for critical cases while avoiding the time loss of verifying every single target manually.
3Loss of time
If reinforcement learning is used to optimize verification strategy, then loss of time is reduced, but device complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where the reinforcement learning agent learns from verification outcomes. The agent receives feedback about which targets required verification and which did not, and uses this information to improve its strategy for dividing targets into subsets and determining verification thresholds. This feedback loop enables the system to optimize verification time while managing complexity through learned patterns rather than rigid rules.
Data Source
AI summary
An apparatus for automatic target recognition with reinforcement learning is provided. The apparatus receives an image of a scene and performs an automatic target recognition on the image to detect objects in the image as candidate targets. The apparatus divides the candidate targets into subsets of candidate targets and performs a verification of the automatic target recognition to identify true targets in the image. In the verification, the apparatus solicits user input to manually identify some true targets in the image. The verification is performed according to a reinforcement learning process to minimize a total verification time.