The conventional methods for inspection of industrial sites involve the revision of data by an experienced inspector during the acquisition process to avoid possible data missing and misinterpretation. Despite all the advantages of drone-based inspection, inspectors often do not easily have physical access to the site to check for any data ambiguity. Therefore, it is essential for autonomous or semi-autonomous systems to check for missing data or to highlight possible data ambiguity. Reflection in thermal imagery data is one of the main sources of misinterpretation, and it can be problematic when there is no physical access to the site for a secondary inspection. In this paper, we present a novel algorithm based on the analysis and stitching of consecutive aerial thermal images to detect areas with reflection effect and possibly reduce these effects. The conducted experiments have shown significant results in the detection of reflection in drone-based thermographic inspections.