Surface defects in galvanized scaffolding such as excess zinc on the metal surface can significantly affect assembly precision and durability. Traditional inspections based on manual visual assessment are limited in efficiency and cannot be easily automated. In this work, a 3D scanning-based method is proposed for automated defect detection in galvanized scaffolding. A 3D scanner is employed to acquire point cloud data of scaffolding components. The scanned point cloud is then aligned with an uncoated 3D Computer-Aided Design (CAD) reference model using the Iterative Closest Point (ICP) algorithm. Defects are identified by analyzing spatial deviations between the scanned data and the reference model. Deviations are considered as defects when exceeding a predefined threshold. To eliminate false positives defects and deviations at irrelevant areas, two region-specific filters are applied. Experimental validation demonstrates the improved automation of the proposed method compared to manual visual inspection.