Adaptive Object Detection Using Reinforcement Learning Chains
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Solution Overview
Problem
Traditional object detection and recognition techniques are not adaptive to changing conditions, consume excessive processing resources, and are inefficient in providing timely decision-making, leading to challenges in processing vast amounts of ISR data effectively.
Innovation Solution
The implementation of Reinforcement Learning (RL) techniques to determine chains of algorithms for object detection and recognition, enabling adaptive and efficient processing by interacting with the environment, using agents to learn through trial and error, and providing feedback for improved accuracy and timeliness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional object detection and recognition techniques are used, then object detection capability is provided, but processing resource bandwidth and time consumption are excessive
Solution Approach 1:
The patent segments the object detection process into multiple stages: initial filtering using simplified criteria, intermediate processing of promising regions, and detailed analysis only for high-probability targets. This hierarchical segmentation allows the system to process vast ISR data volumes by applying computationally intensive algorithms only where necessary, dramatically reducing overall processing resource consumption while maintaining detection effectiveness.
Solution Approach 2:
The system applies partial action by performing comprehensive analysis only on selected regions of interest rather than processing entire images or data streams. By using preliminary filters to identify and isolate potential targets, the system performs excessive (thorough) analysis only where needed, avoiding wasteful processing of clearly negative cases while ensuring complete analysis of promising candidates.
2Measurement precision
If traditional approaches use advanced satellite, radar, and ISR capabilities to collect and fuse mission data, then data collection capability is improved, but search time is limited
Solution Approach 1:
The patent implements preliminary action through pre-computed lookup tables, pre-processed reference data, and advance filtering algorithms that prepare data structures before actual detection occurs. By pre-organizing terrain data, reference object libraries, and processing pipelines, the system minimizes real-time computation requirements, enabling rapid search through vast ISR data without sacrificing detection accuracy.
Solution Approach 2:
The system introduces intermediary processing layers including data fusion algorithms, feature extraction modules, and probability assessment filters that mediate between raw ISR data and final detection results. These intermediaries transform complex multi-source data into simplified representations that can be rapidly searched and evaluated, reducing search time while preserving measurement precision through systematic data transformation.
3Adaptability or versatility
If traditional approaches are used, then object detection is performed, but adaptability to changing conditions and unexpected events is limited
Solution Approach 1:
The patent implements dynamics through adaptive threshold adjustment, dynamic parameter selection, and real-time algorithm switching based on environmental conditions and detection performance. The system continuously adjusts processing parameters, filter sensitivity, and analysis depth according to changing ISR data characteristics, enabling adaptability to varying operational conditions without requiring complete system redesign.
Solution Approach 2:
The system achieves adaptability through parameter changes including adjustable detection thresholds, dynamic confidence level requirements, and configurable processing intensity based on operational context. By modifying processing parameters rather than fundamental system architecture, the system adapts to changing conditions and unexpected events while maintaining manageable complexity through parameter tuning rather than structural transformation.
4Productivity
If traditional approaches are used, then processing is performed, but timeliness for decision making is insufficient
Solution Approach 1:
The patent applies local quality by concentrating high-precision processing resources on specific regions of interest identified through preliminary filtering, rather than applying uniform processing across entire data sets. By allocating computational precision locally to promising target regions while using simplified processing elsewhere, the system achieves both rapid decision-making for critical targets and maintained detection accuracy where it matters most.
Data Source
AI summary
Discussed generally are techniques for managing operation of programs in a sequential order. A method can include receiving a query for an image, the query indicating characteristics of the image, selecting a chain of algorithms configured to identify the image based on the characteristics, operating an algorithm of the selected chain of algorithms that operate in increased fidelity order on an input to produce a first result, operating a ground truth algorithm on the input to generate a second result, comparing the first and second results to determine a probability of correctness (Pc) and confidence interval (CI) for the algorithm, and altering the chain of algorithms based on the determined Pc and CI.