Extraction of asymptotic edges of microcracks in silicon nitride bearings based on adaptive nonlocal mean filtering and iterative tracking algorithm

Abstract

The gradual change edge of the Si₃N₄ bearing roller microcracks with decreasing gray gradients are difficult to be extracted by threshold segmentation. The method for extracting the gradual change edge of Si₃N₄ bearing roller microcracks using adaptive non-local mean filtering and an iterative tracking algorithm is proposed. Widely distributed, large-span, dense noise is eliminated from the gradual change edges of microcracks in Si₃N₄ bearing roller microcrack images. In the iterative expansion process of microcrack defect shape, the gradual change edge pixel of microcrack is accurately tracked. The Si₃N₄ bearing roller microcrack image is enhanced by adaptive non-local mean filtering. The following are the experimental findings: The PSNR and SNR reach 38.12 dB and 40.94 dB, respectively. The microcrack gradual change edge pixels can be extracted with an edge coverage rate of 92.5 % and an accuracy of 93.8 % using the iterative tracking algorithm for Si₃N₄ bearing roller microcrack images.