Assessing the effectiveness of sharpness metrics to determine the presence of contamination on thermographic cameras in harsh environments

Abstract

This article assesses the effectiveness of sharpness metrics for monitoring the cleanliness of in-line thermographic systems and enabling self-diagnosis, in order to prevent degradation of metrologic performance and increase of measurement uncertainty. When optical measurement systems are installed in harsh industrial environments, external contaminants may compromise their operation conditions. Dust settling on the lens or protective window deteriorates the signal quality, reducing the sharpness of thermal images and making it challenging to extract object features accurately. This study investigates whether monitoring image sharpness can serve as an indicator of contamination presence. Contamination was simulated in controlled laboratory experiments by incrementally adding 0.4 g of dust mixture to the lens of a thermal camera observing high-temperature steel objects. Among the metrics evaluated, Histogram Entropy and the Brenner gradient showed monotonic trends and high sensitivity to small amounts of contamination, with slopes greater than 0.25. The uncertainty of these metrics is less than 0.3. The combined metric, derived through multiple linear regression, improved accuracy, with an R² of 0.96, up from 0.93 for Histogram Entropy and 0.95 for Brenner. Validated with real industrial data, the combined metric proved effective for real-time inline contamination diagnostics in manufacturing environments.