An Improved Perspective Transformation and Subtraction Operation for PCB Defect Detection

Defects appeared in the printed circuit board (PCB) will pose a serious damage on the following procedure. Image based inspection methods have been proposed to improve the efficiency and reliability of PCB defect detection compared to manual inspection. The machine learning and deep learning detection methods are popular one, however, they are complex, time consumption and require lots of labeled samples. Thus, we conduct the PCB defect detection and classification by using the template-based algorithm. To realize an accurate registration, the region of interest (ROI) among input image is first computed by utilizing the Grab Cut method. Furthermore, to ensure the complete overlap of feature points between the test image and template image, a perspective transformation based on four vertexes calculating is introduced. Once the different shape and posture images are transformed into a uniform imaging plane, a subtraction operation is used to extract the features of various defects. Experiments on a public data set prove the efficiency of our proposed method.