Defect Inspection Algorithm of Metal Surface Based on Machine Vision

• Published in: International Conference on Measuring Technology and Mechatronics Automation (Print) pp. 45 - 49
• Main Authors: Zhou, Awei, Zheng, Han, Li, Meng, Shao, Wei
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.02.2020
• Subjects: Bi-dimensional Empirical Mode Decomposition; Defect Inspection; Empirical mode decomposition; Image edge detection; Inspection; Machine vision; Mechatronics; Metal Surface; Metals; Production
• ISSN: 2157-1481
• DOI: 10.1109/ICMTMA50254.2020.00017

During the production and post-processing of complex metal parts, surface defects such as scratches, stains, and pits will inevitably occur, which will reduce the yield of metal parts and cause serious economic losses. Complex metal surfaces have complex surface texture characteristics, which leads to the difficulty of detection during the detection process. Therefore, this paper presents a defect inspection algorithm of metal surface based on machine vision. The proposed surface defect inspection algorithm first uses improved bi-dimensional empirical mode decomposition (BEMD)-based extracting algorithm to perform initial extracting of surface defects through filtering out complex textures on the metal surface, while retaining as much effective information as defects as possible. Then, the inspection algorithm applies Canny edge detection operator to detect the edge information of these defects. Finally, the final acquisition of defect edges can be achieved by connecting edge breakpoints and image filling operations. Experimental results on inspection of a variety of the surface defects on parts with metal surface are reported to show the performance of the defect inspection algorithm.