Visual Defect Inspection for Deep-aperture Components with Coarse-to-fine Contour Extraction

This paper investigates automatic quality inspection for the components with a small diameter and deep aperture. An automatic pick-and-place system is constructed, which employs an endoscope to achieve better image quality aiming at the characteristics of the component. A coarse-to-fine contour extraction algorithm with four steps is presented to inspect the component’s quality. First, approximate locations of the targets are estimated using faster region-based convolutional neural networks (faster RCNN). Second, the corresponding edge image is obtained by using the multiscale probability boundary (mPb) detector. Third, edge enhancement is performed, which is based on the Brownian motion model. Fourth, the corresponding contours are finely extracted by edge grouping. A shape analyzing algorithm is utilized to classify the components based on the extracted contours. Comparison experiments fully demonstrate the superiority of the proposed inspection method over existing methods. Meanwhile, successful inspection results on challenging real-world image data prove that the system is of practical significance to industrial applications.