<![CDATA[Corrosion on metal surfaces poses significant risks to structural durability and operational safety in industrial environments. Manual detection is often time-consuming and prone to errors, especially under inconsistent lighting. This research addresses the challenge by implementing a color-based segmentation technique using the HSV (Hue, Saturation, Value) color space. The input images undergo preprocessing and conversion from RGB to HSV to enhance color differentiation. A defined range of HSV values, corresponding to typical rust tones, is used to generate binary masks that highlight corroded regions. The algorithm is built using the OpenCV library to support real-time analysis. A series of test images with varying illumination and corrosion patterns were analyzed to assess the method’s effectiveness. The results confirm that the HSV-based segmentation accurately separates rusted areas from unaffected surfaces, maintaining reliable performance under diverse conditions. The proposed approach offers a lightweight and efficient tool for early rust detection, supporting preventive maintenance strategies in industrial inspection workflows.]]>
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