Multi-Scale and Irregularly Distributed Circular Hole Feature Extraction from Engine Cylinder Point Clouds

The circular hole structures on automotive engines possess stringent mechanical processing requirements, so it is of vital importance to perform quality inspections on all manufactured circular hole structures. The detection of circular holes on automotive engines presents a significant challenge due to their numerous, multi-scale, and irregular distribution. Additionally, the data pertaining to circular holes is often incomplete, further complicating the detection process. In this paper, we proposed a multi-scale and irregularly distributed circular hole detection method for engine cylinder blocks, which enables the efficient extraction of all hole feature points within the engine, thereby facilitating quality inspection. First, the utilization of compartmentalization analysis techniques enhances the perceptual capacity for internal hole features from various angles. Second, by employing curvature center contractility method, hole-wall points are contracted towards their circular center positions, further enhancing the identification accuracy of small holes and holes with missing data. The proposed method is tested on both synthetic data and raw data, and compared with existing extraction and circular hole fitting methods. The experiment results demonstrate that compared to other methods, our method achieves the best feature point detection accuracy and hole primitive parameter calculation accuracy. Notably, even in special situations such as those with insufficient hole points and rounded structures, our method maintains exceptional discriminative capability and stability.