An Adaptive Fitting Approach for the Visual Detection and Counting of Small Circular Objects in Manufacturing Applications

Detecting, localizing and counting small circular objects in machine parts is an important task in many applications for manufacturing. Existing methods of circle detection face difficulties due to the high-curvature and limited edge points of circles. As a result, in this paper we propose a novel two-stage circle detection method, which integrates bottom-up coarse detection and top-down circle fitting. First, a circle detector combining low-level feature descriptors and a linear SVM is developed. This is used to scan an input image in a sliding window mode to detect small circles with coarse estimates of locations and scales. Next, a hierarchical Bayesian model performs a top-down adaptive circle fitting, with the ability to achieve a maximum a posteriori probability to fit circles to local image features. The evaluation of our approach with manufacturing images has demonstrated to be efficient in detecting small circles in machine parts.