Defect detection of PCB based on Bayes feature fusion

With the continuous development of the electronics industry, the number of printed circuit board (PCB) has grown at a rapid rate, and the requirements for the detection systems of PCB have also continuously increased. In the traditional PCB detection, the main reference is the comparison method. However, in a real scene, there are a series of problems such as non-uniform illumination, tilting of the camera angle, and the like, resulting in a less satisfactory effect of the reference comparison method. So, the authors proposed a non-reference comparison framework of PCB defects detection. This framework has achieved good results in speed and accuracy. The authors extract the histogram of oriented gradients and local binary pattern features for each PCB image, respectively, put into the support vector machine to get two independent models. Then, according to Bayes fusion theory, the authors fuse two models for defects classification. The authors have established a PCB data set that includes both defective and defect-free. It has been verified that the accuracy of the verification set is improved compared to the individual features using the fused features. The authors also illustrate the effectiveness of Bayes feature fusion in terms of speed.