PCB Defect Classification Using Logical Combination of Segmented Copper and Non-copper Part

In this paper, a new model for defect classification of PCB is proposed which is inspired from bottom-up processing model of perception. The proposed model follows a non-referential based approach because aligning test and reference image may be difficult. In order to minimize learning complexity at each level, defect image is segmented into copper and non-copper parts. Copper and non-copper parts are analyzed separately. Final defect class is predicted by combining copper and non-copper defect classes. Edges provide unique information about distortion in copper disc. In this model, circularity measures are computed from edges of copper disc of a Copper part. For non-copper part, color information is unique for every defect type. A 3D color histogram can capture the global color distribution. The proposed model tries to compute the histogram using nonuniform bins. Variations in intensity ranges along each dimension of bins reduce irrelevant computations effectively. The bins dimensions are decided based on the amount of correlation among defect types. Discoloration type defect is analyzed independently from copper part, because it is a color defect. Final defect class is predicted by logical combination of defect classes of Copper and Non-copper part. The effectiveness of this model is evaluated on real data from PCB manufacturing industry and accuracy is compared with previously proposed non-referential approaches.