Object Identification Tuning for Fast Targeting Adjustment
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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 nature of conventional methods for adjusting object detection algorithms, which often require multiple iterations and retraining of machine learning models.
Innovation Solution
A system and method that allows for immediate adjustment of parameters using user input, without retraining the pre-trained machine learning model, to enhance object detection and targeting by incorporating a decision algorithm that generates recommendations based on user feedback, enabling faster and potentially more accurate object identification and targeting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to adjust object detection algorithms, then the system can achieve accurate object identification, but the process requires multiple iterations and retraining of machine learning models, which is time-consuming
Solution Approach 1:
The system changes parameters of the pre-trained machine learning model based on user feedback without requiring full retraining. The tuning algorithm adjusts model parameters iteratively, allowing rapid adaptation to new objects or environments while maintaining identification accuracy.
Solution Approach 2:
The system incorporates user feedback loops where users provide corrections or confirmations about detected objects. This feedback is used by the tuning algorithm to refine model parameters in real-time, enabling continuous improvement of detection accuracy without time-consuming retraining cycles.
2Adaptability or versatility
If the pre-trained machine learning model is retrained to improve detection accuracy, then the system adapts better to specific environments, but the retraining process is computationally intensive and time-consuming
Solution Approach 1:
Instead of retraining the entire model, the system selectively adjusts specific parameters based on user feedback. This allows the model to adapt to new environments and objects by modifying only the necessary parameters, maintaining high productivity while achieving environmental adaptation.
Solution Approach 2:
The system performs partial retraining by adjusting only the parameters that need modification rather than retraining the entire model. This partial action approach maintains adaptability while significantly reducing computational time and resource requirements.
3Reliability
If multiple iterations of model adjustment are performed to improve detection accuracy, then the system achieves better performance, but the complexity of the adjustment process increases
Solution Approach 1:
The system performs self-adjustment of parameters through automated tuning algorithms that process user feedback and modify model parameters without requiring complex manual intervention. This self-service capability maintains high detection accuracy while reducing operational complexity.
Solution Approach 2:
The tuning algorithm acts as an intermediary between user feedback and model parameters. It automatically processes feedback and translates it into appropriate parameter adjustments, simplifying the overall adjustment process while maintaining detection accuracy through multiple iterations when necessary.
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.