Machine Learning Target Detection Parameter Optimization
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Solution Overview
Problem
Current target detection algorithms require manual optimization of parameters by experts, which is time-consuming, costly, and often results in sub-optimal performance due to limited processing and communication resources, especially in resource-constrained systems like satellites and drones.
Innovation Solution
The use of machine learning-based techniques to generate and optimize configuration parameters for processing windows within images, allowing for the allocation and positioning of these windows based on statistical analysis of image regions to enhance target detection probability while minimizing false alarms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual optimization of parameters by experts is used, then detection accuracy can be improved, but time consumption and cost increase significantly
Solution Approach 1:
The system performs self-optimization of detection parameters by automatically analyzing image statistics and allocating processing windows without requiring expert manual intervention. The algorithm independently adjusts configuration parameters based on statistical features extracted from the image data, enabling the system to serve itself in the parameter optimization process.
Solution Approach 2:
The system dynamically changes configuration parameters such as processing window allocation and detection thresholds based on statistical analysis of image regions. By computing statistics like mean, variance, and skewness for different image regions and using these to adaptively adjust detection parameters, the system achieves high detection accuracy without manual expert tuning.
2Measurement precision
If manual optimization of parameters by experts is used, then detection accuracy can be improved, but system cost increases
Solution Approach 1:
The system eliminates the need for expensive expert consultants by implementing self-service parameter optimization. The algorithm automatically analyzes image statistics and configures detection parameters independently, removing the dependency on human experts and thereby reducing system costs while maintaining detection accuracy.
Solution Approach 2:
The patent replaces the mechanical process of manual expert optimization with an automated computational system. By substituting human expert analysis with algorithmic statistical processing and automatic parameter adjustment, the system reduces costs associated with expert time and manual intervention while achieving comparable or superior detection performance.
3Reliability
If processing windows are allocated based on statistical analysis, then false alarms are reduced, but processing complexity increases
Solution Approach 1:
The system segments the image into multiple processing regions and computes statistics for each region independently. By dividing the image into manageable segments and analyzing statistical features (mean, variance, skewness) for each segment, the system allocates processing windows to regions most likely to contain targets, thereby reducing false alarms while keeping individual region processing relatively simple.
Solution Approach 2:
The system applies different processing strategies to different image regions based on their local statistical characteristics. Regions with statistical features indicating high probability of containing targets receive more processing windows and more intensive analysis, while other regions receive fewer resources. This local quality approach reduces overall false alarms by concentrating resources where they are most needed.
Data Source
AI summary
A method includes obtaining an image of a scene and identifying one or more statistics associated with each of multiple processing regions within the image, where each processing region represents a portion of the image. The method also includes generating a probability of each of the processing regions containing at least one object of interest based on the statistics associated with the processing regions. The method further includes allocating multiple processing windows to one or more of the processing regions based on the probabilities, where the processing windows are smaller than the processing regions. In addition, the method includes performing object detection within the allocated processing windows.