Object Identification Tuning for Real-Time Targeting Accuracy
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Solution Overview
Problem
Autonomous systems struggle to accurately identify and locate objects in unpredictable environments, such as cluttered rooms or city streets, due to the time-consuming and inefficient process of adjusting object detection algorithms, which often requires in-field testing and retraining of machine learning models.
Innovation Solution
A method and system that allows for real-time adjustment of parameters using user input, without retraining pre-trained machine learning models, to enhance object detection and targeting, utilizing a decision algorithm separate from the machine learning model to generate recommendations for improved object detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If in-field testing and retraining of machine learning models is performed to improve object detection accuracy, then detection precision is improved, but time consumption and system complexity increase
Solution Approach 1:
The system separates the machine learning model from the parameter adjustment mechanism. The pre-trained model remains fixed while a separate parameter tuning system handles adaptations, allowing rapid modifications without retraining the entire model.
Solution Approach 2:
The system enables dynamic adjustment of detection parameters (such as confidence thresholds, detection sensitivity, and targeting parameters) without modifying the underlying machine learning model weights, achieving adaptation through parameter tuning rather than model retraining.
2Measurement precision
If in-field testing and retraining of machine learning models is performed to improve object detection accuracy, then detection precision is improved, but device complexity increases
Solution Approach 1:
The system divides functionality into a fixed machine learning model component and a separate parameter adjustment component. This segmentation allows the complex model to remain unchanged while simpler parameter adjustments handle adaptability needs.
Solution Approach 2:
A parameter tuning system acts as an intermediary between user input and the machine learning model, translating operational requirements into parameter adjustments without requiring direct model modification or retraining.
3Speed
If pre-trained machine learning models are used for object detection, then detection speed is improved, but adaptability to new environments deteriorates
Solution Approach 1:
The system maintains the speed advantage of pre-trained models by enabling environmental adaptation through parameter adjustments rather than model retraining, allowing rapid deployment in new contexts.
Solution Approach 2:
The system introduces dynamic parameter adjustment capabilities that allow the detection system to adapt to changing environments in real-time, transforming a static model into a dynamically adaptable system without sacrificing processing speed.
Data Source
AI summary
In some variations, a method for enhancing identification and/or targeting of an object of interest includes providing a sample image of a sample object to a user, receiving an indication from the user identifying the sample object, and generating, via a tuning algorithm, a recommended change to one or more parameters based on the indication from the user, and modifying the one or more parameters based on the recommended change. The one or more parameters may be used by a decision algorithm, where the decision algorithm is configured to instruct an action associated with an object of interest in one or more images, based on (i) a pre-trained machine learning model that characterizes the object of interest in the one or more images, and (ii) the one or more parameters.