Surface Defect Detection of Steel Balls Based on Surface Full Expansion and Image Difference

The steel ball, as a key rolling element in mechanical equipment, directly affects the performance and service life of the bearing through its surface quality. Traditional methods for detecting surface defects on steel balls often face challenges in efficiency and stability. They struggle with three-dimensional surfaces and are easily affected by noise interference. This paper proposes a defect detection method for the steel ball surface based on Axial Cone mirror expansion and Improved Image Difference (ACID). The axial cone mirror unfolds the entire surface of the steel ball, allowing complete surface images to be obtained with just two captures. This simplifies the acquisition process and increases efficiency. The improved image difference method, combined with adaptive thresholding and adjacent frame difference techniques, effectively reduces noise interference. It enhances both the accuracy and robustness of defect detection. Frequent threshold adjustments and unstable detection, common in traditional methods, are avoided. Experimental results demonstrate that the ACID-based detection method surpasses traditional methods in terms of efficiency and accuracy. The improved method significantly reduces the missed detection rate. For example, the detection rates for cluster, scratch, and stain have increased from 86%, 80%, and 84% to 98%, 96%, and 98%, respectively. Moreover, the improved method reduces noise interference, avoids frequent threshold adjustments, simplifies the operation process, and shows higher stability and robustness in complex background conditions.