Semiautomatic Mask Generating for Electronics Component Inspection

This article proposes a new method for semiautomatically generating the image Mask required for training Mask R-CNN. Since manual labeling is very time-consuming and laborious to obtain the image mask, we propose a very simple and fast method based on graph cut to obtain image Mask method, which uses graph cut-based image segmentation to output pixel-level segmentation results and obtain Mask of the input image through image transform, and then, the Mask R-CNN-based surface defect detection method is implemented, which includes three different branches, namely, the boundary box regression and positioning branch, the boundary box classification branch, and the segmentation branch, to realize the function of locating, classifying, and segmenting defects at the same time. The experimental results confirm the effectiveness of our proposed method; under the premise of ensuring the detection accuracy of the Mask R-CNN method, the Mask required for training Mask R-CNN can be quickly and simply obtained.