Research on defect segmentation algorithm of industrial CT image after Faster R-CNN positioning

The defect area located by Faster R-CNN has weak edges. The area would be over-segmented or under-segmented if conventional segmentation algorithm is adopted. This paper made an analysis on precise threshold segmentation algorithm for workpiece defects based on Faster R-CNN location, reconstructing the localization area by morphological opening and closing reconstruction algorithm, processing the reconstructed image by Otsu′s dual threshold method, segmenting transformed images by maximum entropy threshold segmentation method, and finally measuring the area, perimeter and other parameters of the segmented defects. The research shows that the algorithm in this paper has higher segmentation ability regarding workpiece defects(crack, bubble and slag), compared to conventional algorithms. It not only can accurately segment objects with weak edges, but also can effectively remove the interference from the contour background to the segmentation.