Surface moisture detection using thermal imaging and computer vision

Abstract

Thermal imaging is used to detect moisture inside surfaces such as walls or floors by showing the temperature difference between the moisture and the structure. Surface moisture detection can be critical in quality assurance, healthcare, construction and agriculture. This paper aims to extend the usage of thermal imaging and computer vision to detect the coverage of moisture on the surface using computer vision rather than relying on an end user. This process relies on the thermal properties of the liquid that is sprayed on a surface, which would have a distinct temperature difference compared to the surface it is on. The methodology proposed in this paper is to utilize an infrared thermal image camera to analyze the surface. Then, using computer vision, the output is processed to detect the areas of the largest temperature gradients while filtering the noise. This ensures only areas with a large enough gradient are highlighted, capturing the sprayed surface. These areas are converted to a percentage of the captured area and displayed to the user. Preliminary findings from the experiments show that the system is able to detect liquids that have a temperature difference of at least 5 deg C (9 deg F). As this method only relies on thermal imaging, it is a non-destructive and non-invasive test, where the user does not need to interact with the surface or the liquid directly. The information provided by the technology can contribute to fault detection and quality control when it comes to spray coverage.