Infrared Thermography Measurement Based on Machine Vision is a measurement method that combines computer vision technology with infrared thermography. This method generally involves the following steps:
Using an infrared thermal imager to capture infrared images of the target object.
Performing preprocessing operations such as noise reduction and enhancement on the image to improve the effectiveness of subsequent processing.
Using machine learning or deep learning algorithms to detect and recognize targets in the image. In infrared thermography measurement, the targets are usually areas with temperature anomalies, such as hotspots or cold spots.
Calculating the temperature values of the target areas based on the grayscale or pixel values of the infrared image, combined with the calibration parameters of the thermal imager.
Displaying the measured temperature information on the image to facilitate user analysis and decision-making.
This method can be widely used in industries, healthcare, security, and other fields, enabling fast and accurate measurement and analysis of temperature distributions.
