Infrared Imaging System Machine Learning Image Setting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Infrared imaging systems face challenges in determining optimal image settings automatically and accurately, leading to potential errors in temperature measurements and fault detection in industrial environments.
Innovation Solution
The implementation of a machine learning model, such as a neural network, within an infrared imaging system to determine image settings based on captured data, incorporating user input for training and iterative improvements, allowing for automatic setting determination and enhanced accuracy through continuous learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automatic image setting determination is implemented using machine learning, then image setting accuracy is improved, but system complexity increases
Solution Approach 1:
The system performs self-service by automatically determining image settings using a machine learning model trained on historical data, eliminating the need for manual configuration while maintaining high accuracy. The model autonomously processes image data and outputs optimized settings without human intervention.
Solution Approach 2:
The system incorporates feedback mechanisms where user corrections to automatically determined settings are captured and used to retrain the machine learning model. This continuous feedback loop improves the model's accuracy over time while maintaining the automation benefits.
2Device complexity
If manual image setting adjustment is used, then system complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The system automatically determines optimal image settings including temperature measurements without requiring manual adjustment, thereby maintaining high measurement precision while avoiding the complexity of manual configuration processes.
Solution Approach 2:
The machine learning model dynamically adjusts image settings parameters such as gain, offset, and temperature thresholds based on input image data and training data, optimizing measurement precision automatically without manual parameter tuning.
3Measurement precision
If iterative training with user input is implemented, then model accuracy is improved, but time consumption increases
Solution Approach 1:
The system performs preliminary action by pre-training the machine learning model on historical image data and settings before actual use. This preliminary training equips the model with foundational accuracy, reducing the need for extensive real-time training during operation.
Solution Approach 2:
The system uses feedback from user corrections to refine the model iteratively. User input on automatically determined settings provides valuable feedback that updates the training data, progressively improving model accuracy without requiring complete retraining from scratch.
4Productivity
If automatic setting determination is used, then productivity is improved, but reliability may deteriorate due to potential errors
Solution Approach 1:
The system incorporates feedback mechanisms where user corrections to automatically determined settings are captured and used to retrain the machine learning model. This continuous feedback loop improves the model's accuracy and reliability over time while maintaining the automation benefits for high productivity.
Solution Approach 2:
The machine learning model dynamically adjusts image settings parameters such as gain, offset, and temperature thresholds based on input image data and training data, optimizing measurement precision automatically without manual parameter tuning.
Data Source
AI summary
Techniques for facilitating image setting determination and associated machine learning in infrared imaging systems and methods are provided. In one example, an infrared imaging system includes an infrared imager, a logic device, and an output/feedback device. The infrared imager is configured to capture image data associated with a scene. The logic device is configured to determine, using a machine learning model, an image setting based on the image data. The output/feedback device is configured to provide an indication of the image setting. The output/feedback device is further configured to receive user input associated with the image setting. The output/feedback device is further configured to determine, for use in training the machine learning model, a training dataset based on the user input and the image setting. Related devices and methods are also provided.