A machine vision approach for detecting and inspecting circular parts

In this paper, we present a machine vision approach for detecting and inspecting circular parts and the parts with circular arcs on the contours. The method uses the Hough transform technique and utilizes the directional information of a normal to the circle at each boundary point. A cubic polynomial curve fitting is used to estimate the normal and determine the concavity of the fitted curve at each given boundary point. The proposed Hough transform method is a two-stage procedure. The first stage of the procedure uses a 2-D accumulator array to detect circle centers. Then the second stage uses a 1-D accumulator array to detect the radii of circles. The proposed method is robust to detect circular parts with partial occlusion such as peripheral defects or burrs. For an image of size N x N, the storage requirements are N and the time complexity is bounded by (N+m)n, where m is the number of circle centers detected in the first stage and n is the number of boundary points in the image.