AI Gateway Architecture for Selective Multi-Model Classification
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Solution Overview
Problem
Current robotic systems equipped with cameras and recording equipment face challenges in efficiently detecting specific objects within video or audio streams using machine learning models, especially when resources are limited and third-party models are required.
Innovation Solution
The implementation of an AI gateway system that processes images through multiple machine learning models, allowing for the selection of specific object types to be detected and optimizing resource usage by throttling or shutting down less critical models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple machine learning models are deployed to detect various object types, then the detection capability and versatility are improved, but the computational resource consumption and device complexity increase
Solution Approach 1:
The system dynamically adjusts the set of active machine learning models based on operational context, resource availability, and detection priorities. The controller can enable or disable specific models in real-time, transforming the static resource allocation into a dynamic adaptation mechanism that balances detection capability with computational constraints.
Solution Approach 2:
The detection system is segmented into multiple independent machine learning models, each specialized for specific object types. This allows selective activation of only the necessary models for current operational needs, rather than running all models continuously, thereby reducing overall computational resource consumption while maintaining versatility.
2Adaptability or versatility
If third-party machine learning models are integrated to expand detection options, then the adaptability is improved, but the device complexity and resource requirements worsen
Solution Approach 1:
The controller is designed with universal compatibility to host and manage multiple third-party machine learning models with different detection specializations. This multi-functional architecture allows the same hardware platform to adapt to various detection needs by loading appropriate models, expanding detection options without proportionally increasing permanent resource requirements.
Solution Approach 2:
The controller acts as an intermediary layer between the unmanned vehicle's processing capabilities and multiple third-party machine learning models. It manages model loading, execution, and resource allocation, allowing flexible integration of external detection capabilities while shielding the underlying resource constraints and complexity from the operational system.
3Measurement precision
If all available machine learning models are executed simultaneously, then the detection precision is improved, but the operational duration is reduced due to higher energy consumption
Solution Approach 1:
The system implements partial execution of machine learning models by activating only the subset necessary for current detection requirements rather than running all available models. This partial action approach maintains adequate detection precision for operational needs while significantly reducing energy consumption to extend operational duration.
Solution Approach 2:
The system employs periodic reassessment of which machine learning models to execute, adjusting the active model set based on changing operational conditions, detected object types, and energy levels. This periodic adaptation allows the system to maintain detection precision when needed while conserving energy during periods when fewer models are required.
Data Source
AI summary
Methods and systems are described herein for hosting and arbitrating algorithms for the generation of structured frames of data from one or more sources of unstructured input frames. A plurality of frames may be received from a recording device and a plurality of object types to be recognized in the plurality of frames may be determined. A determination may be made of multiple machine learning models for recognizing the object types. The frames may be sequentially input into the machine learning models to obtain a plurality of sets of objects from the plurality of machine learning models and object indicators may be received from those machine learning models. A set of composite frames with the plurality of indicators corresponding to the plurality of objects may be generated, and an output stream may be generated including the set of composite frames to be played back in chronological order.