Product surface defects can adversely affect product quality, appearance, and performance. For manufacturers, to meet customer requirements for product surface quality, especially sheet metal shells, covers or sheet metal that needs to be painted and coated, it is usually necessary to manually inspect for defects on the metal surface.
Developed by Hong Kong Productivity Council (HKPC), this system uses a deep-learning and computer vision method to detect surface defects, reducing the impact of human cognitive bias and poor real-time performance on defect detection. This system can significantly reduce the inspection time and human resources for surface defect detection of sheet metal parts.
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