Neural Network Cascade for Real-Time Scene Compliance
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Solution Overview
Problem
Neural networks used in real-time applications face challenges in providing accurate, live feedback due to time constraints, making it difficult to meet real-time operation requirements in applications such as image analysis.
Innovation Solution
A system and method utilizing a user device with processing circuitry and sensors to run neural networks on video frames to detect application-specific requirements, providing directions to adjust elements in the scene until the requirements are met, with multiple neural networks trained on different datasets to ensure compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks are used for real-time image analysis, then detection accuracy is improved, but processing time increases making real-time operation impossible
Solution Approach 1:
The patent divides the neural network processing into multiple stages: a first neural network performs quick detection of basic features, and a second neural network performs more complex analysis only when needed. This segmentation allows the system to achieve high detection accuracy for complex cases while maintaining fast processing for simple cases, resolving the contradiction between accuracy and speed.
Solution Approach 2:
The system applies partial neural network processing by using a lightweight first neural network for all images and only invoking a more computationally intensive second neural network when the first network's confidence threshold is not met. This partial application of heavy processing only where necessary maintains real-time performance while achieving high accuracy when needed.
2Manufacturing precision
If multiple neural networks are used to detect different requirements, then compliance accuracy is improved, but system complexity increases
Solution Approach 1:
The patent employs a universal neural network architecture where the first neural network can detect multiple types of features (faces, poses, environmental conditions) and the second neural network refines detection across all these categories. This multi-functional design achieves high compliance accuracy for various requirements while avoiding the complexity of completely separate specialized networks for each detection task.
Data Source
AI summary
A system, device, and a method for guiding a user to comply with one or more application-specific requirements by using sequentially two or more neural networks run on one more video frame of a scene to detect at least one requirement of the one or more application-specific requirements. Upon the detection result, the application guides a user to adjust the scene based on the detection until the scene is adjusted to meet the application-specific requirements.