Infrared Camera ADC-to-Temperature Conversion Uncertainty Quantification

Sensors such as the FLIR Ax5 family of thermal imaging cameras are an important part of many industrial, manufacturing, and autonomous systems. This use case example quantifies the ease of implementation and the speed of execution, of automating the uncertainty quantification (UQ) of sensor data conversion firmware for the FLIR Ax5. The evaluation runs the software of the use case over an instance of the Signaloid Compute Engine. The output of the execution is both the usual calibrated temperature output as well as programmatically-accessible information on the uncertainty of the calibrated temperature outputs as a function of the measurement uncertainty in the raw sensor transducer voltage output. Such UQ can provide valuable design-time as well as real-time safety information. The implementation overhead and execution cost of the status quo method of Monte Carlo simulation however discourages many organizations from implementing UQ for sensor data conversion routines. FLIR provides public documentation of the mathematical operations they perform on the raw radiometric data to convert them to temperatures and their cameras provide access to the raw radiometric data as well as the necessary complementary calibration parameters. Based on the implied uncertainty given by the number of significant digits in the calibration values provided by FLIR to set the default calibration parameter uncertainty for their Ax5 cameras, combined with propagating that through the equations for converting between radiometric counts and calibrated temperature readings, there is uncertainty in the object temperature.