Printed Circuit Board Defect Detection Using Mathematical Morphology and MATLAB Image Processing Tools
various concentrated work on detection of defects on printed circuit boards (PCBs) have been done, but it is also crucial to classify these defects in order to analyze and identify the root causes of the defects. This project is aimed in detecting and classifying the defects on bare single layer PCBs by introducing a hybrid algorithm by combining the research done by Heriansyah et al |1| and Khalid |2|. This project proposes a PCB defect detection and classification system using a morphological image segmentation algorithm |l| and simple the image processing theories |2|. Based on initial studies, some PCB defects can only exist in certain groups. Thus, it is obvious that the image processing algorithm could be improved by applying a segmentation exercise. This project uses template and test images of single layer, bare, grayscale computer generated PCBs. The research improves Khalid |2| work by increasing the number of defect categories from 5 to 7, with each category classifying a minimum of 1 to a maximum 4 different types of defects and a total of 13 out of 14 defects were classified.
Morphological Segmentation Image Processing
S.H Indera Putera Z.Ibrahim
Faculty of Electrical Engineering Universiti Teknologi MARA, 40450 Shah Alam, Selangor Malaysia
国际会议
上海
英文
359-363
2010-06-22（万方平台首次上网日期，不代表论文的发表时间）