  This study addresses the challenge of accurately seg-menting rusted areas on
painted construction surfaces. A method leveraging digital image processing is
explored to calculate the percentage of rust present on painted coatings. The
proposed segmentation approach is based on the HSV color model. To equalize
luminosity and mitigate the influence of illumination, a fundamental model of
single-scale Retinex is applied specifically to the saturation component.
  Subsequently, the image undergoes further processing, involv-ing manual color
filtering. This step is crucial for refining the identification of rusted
regions. To enhance precision and filter out noise, the pixel areas selected
through color filtering are subjected to the DBScan algorithm. This multi-step
process aims to achieve a robust segmentation of rusted areas on painted
construction surfaces, providing a valuable contribution to the field of
corrosion detection and analysis.
