Behrooz-elahi asked . 2021-06-01

Train a deep learning network for recognizing the image temperature

I am trying to calibrate my high speed camera for measuring the liquid steel temperature, I've decide to record from 800 to 1500 Celsius images correspond to their temperature and then recognize the unknown liquid steel temperature, is there any pre-trained network for doing it?

deep learning , image processing , image analysis , calibration , data , network , temperature , the

Expert Answer

Prashant Kumar answered . 2024-12-21 20:35:31

I don't understand why you need a network for that when you just said the value of each pixel is proprotional to the temperature in degrees Celsius at that pixel location. Simply present the camera with a bunch of vats of liquid across your temperature range and get the mean gray level with mean2(). Now you have a calibration look up table of gray level vs. temperature. You can fit this to something like a quadratic:

coefficients = polyfit(meanGrayLevels, knownTemperatures, 2);

Now to get the temperature of an unknown pixel or image, simply put the temperature into polyval:

estimatedTemperature = polyval(coefficients, meanOfUnknown);

This is SO much easier than using a deep learning network!

Don't fall into the trap of the single tool people who think the deep learning hammer is needed for every job. It might not even be the best tool. You need to consider what really needs to be done and not just apply deep learning to everything. It's not always the right tool. I do color, thermal, and radiometric calibration all the time so I know a bit about it, to say the least.

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