AI Gateway Parallelization for Resource-Limited Object Recognition
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Solution Overview
Problem
Unmanned systems with limited computation resources face challenges in using third-party machine learning models for object detection, as they require efficient resource management to operate effectively in operational scenarios.
Innovation Solution
An AI gateway system processes images through multiple machine learning models, allowing operators to select specific object types for detection, and dynamically manages resource utilization by activating only necessary models, adjusting input based on processing speed and resource thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple machine learning models are used for object detection in unmanned systems, then object recognition capability is improved, but resource consumption increases
Solution Approach 1:
The system dynamically activates or deactivates machine learning models based on operational conditions and resource availability. The AI gateway controller monitors system state and adjusts which models are active, allowing the unmanned system to adapt its computational resource usage to match current operational needs while maintaining the capability to use multiple models when resources permit.
Solution Approach 2:
The system changes operational parameters such as model activation states, processing frequency, and resource allocation thresholds to optimize the balance between object recognition capability and resource consumption. By adjusting these parameters dynamically, the system can scale its AI processing capacity up or down based on available resources.
2Adaptability or versatility
If third-party machine learning models are deployed on unmanned vehicles, then detection functionality is improved, but device complexity increases
Solution Approach 1:
The AI gateway controller serves as an intermediary between multiple third-party machine learning models and the unmanned vehicle's operational systems. It manages model loading, execution, and output integration, abstracting the complexity of running multiple external models away from the core vehicle systems while enabling diverse detection capabilities.
Solution Approach 2:
The AI gateway controller is designed as a universal platform capable of hosting and coordinating multiple different third-party machine learning models with varying requirements. This multi-functional architecture allows the system to integrate diverse detection functionalities from different sources without requiring separate integration mechanisms for each model.
3Productivity
If all machine learning models are activated simultaneously, then object detection coverage is improved, but operational duration decreases
Solution Approach 1:
The system activates only the necessary subset of machine learning models required for current operational needs rather than running all available models simultaneously. The AI gateway controller evaluates which models are needed based on operational context, resource availability, and detection priorities, activating only sufficient processing capacity to maintain operational duration.
Solution Approach 2:
The system periodically reassesses which machine learning models should be active based on changing operational conditions and resource levels. Rather than maintaining a static configuration, the AI gateway controller implements periodic evaluation and adjustment of model activation states, allowing the system to extend operational duration by reducing processing load when full detection coverage is not 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.