Automated extraction of PCB components based on specularity using layered illumination

This paper investigates the methodologies for locating and identifying components on a printed circuit board (PCB) used for surface mount device inspection. It’s the foundation of other inspections, such as solder joint inspection, component type recognization and so on. The proposed scheme consists of two stages: solder joint extraction and protective coating extraction. This work uses automatic multilevel thresholding approach for detecting specular areas which contain solder joints. Some invalid specular areas, such as markings and via-holes are recognized and removed by comparing the colour distribution features of the target objects and the reference objects. A novel approach based on connection graph and the segmented gray-scale PCB images is developed to classify all recognized solder joints as several clusters. And then, the protective coating is extracted by the positions of the clustered solder joints. Experimental results show that the proposed method can recognize most of components effectively.

[1]  R. Redner,et al.  Mixture densities, maximum likelihood, and the EM algorithm , 1984 .

[2]  Alan Crispin,et al.  Automated inspection of PCB components using a genetic algorithm template-matching approach , 2007 .

[3]  Young Shik Moon,et al.  Automatic inspection of solder joints using layered illumination , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[4]  Pei-Chann Chang,et al.  A case-based evolutionary model for defect classification of printed circuit board images , 2008, J. Intell. Manuf..

[5]  Shaogang Gong,et al.  Tracking colour objects using adaptive mixture models , 1999, Image Vis. Comput..

[6]  Ramesh C. Jain,et al.  Automatic Solder Joint Inspection , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  P. KaewTrakulPong,et al.  An Improved Adaptive Background Mixture Model for Real-time Tracking with Shadow Detection , 2002 .

[8]  Young Shik Moon,et al.  Visual inspection system for the classification of solder joints , 1999, Pattern Recognit..

[9]  Bernard C. Jiang,et al.  Machine vision and background remover-based approach for PCB solder joints inspection , 2007 .

[10]  Sang Wook Lee,et al.  Detection of specularity using colour and multiple views , 1992, Image Vis. Comput..

[11]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[12]  Stephen Lin,et al.  Diffuse-Specular Separation and Depth Recovery from Image Sequences , 2002, ECCV.

[13]  Shaogang Gong,et al.  Segmentation and Tracking Using Color Mixture Models , 1998, ACCV.

[14]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[15]  Ching-Yuen Chan,et al.  A particle swarm optimization approach for components placement inspection on printed circuit boards , 2009 .

[16]  Shyang Chang,et al.  A new criterion for automatic multilevel thresholding , 1995, IEEE Trans. Image Process..

[17]  Fernando Torres Medina,et al.  Automatic Detection of Specular Reflectance in Colour Images Using the MS Diagram , 2003, CAIP.

[18]  Dinesh P. Mital,et al.  Automated visual inspection of surface mount PCBs , 1990, [Proceedings] IECON '90: 16th Annual Conference of IEEE Industrial Electronics Society.

[19]  Steven A. Shafer,et al.  Using color to separate reflection components , 1985 .