基于耦合去噪算法的航空发动机中Si3N4圆柱滚子表面缺陷的检测方法

为解决基于机器视觉的传统单一图像去噪算法对混合噪声信号处理效果不佳，导致不能有效地检测识别航空发动机中应用的Si₃N₄圆柱滚子表面缺陷问题，提出一种基于改进的耦合去噪算法与多尺度阈值分割算法相结合的视觉检测方法。通过优化的小波阈值去噪算法与改进的中值滤波算法相耦合方法对Si₃N₄圆柱滚子的表面缺陷图像进行去噪处理，采用多尺度阈值分割算法对缺陷图像进行图像分割，识别提取Si₃N₄圆柱滚子表面缺陷。实验结果表明：Si₃N₄圆柱滚子表面缺陷图像经过改进的耦合去噪算法进行去噪后，信噪比>24.5%，多尺度阈值分割算法对Si₃N₄圆柱滚子表面缺陷图像的检测识别准确率>94%；该视觉检测方法具有良好的图像去噪效果，为进一步图像的缺陷识别打下基础，并且具有一定的通用性。

Detection and Analysis of Surface Defects of Si3 N4 Cylindrical Roller in Aero-engine Based on Coupled Denoising Algorithm

In order to solve the problem that the traditional single image denoising algorithm based on machine vision has poor effect on the mixed noise signal processing, resulting in the inability to effectively detect and identify the surface defects of Si₃N₄ cylindrical roller used in aero-engine, a visual detection method based improved coupled denoising algorithm and multi-scale threshold segmentation algorithm is proposed. The surface defect image of Si₃N₄ cylindrical roller is denoised by the optimized wavelet threshold denoising algorithm and the improved median filter algorithm, and the multi-scale threshold segmentation algorithm is used to segment the defect image, thus identifying and extracting Si₃N₄ surface defects of cylindrical rollers. The experimental results show that the signal-to-noise ratio of surface defect images of Si₃N₄ cylindrical rollers denoised by the improved coupling denoising algorithm is more than 24.5%, and the detection and recognition accuracy rate of the multi-scale threshold segmentation algorithm for surface defect images of Si₃N₄ cylindrical rollers is more than 94%. It proves that the visual detection method has a good image denoising effect and a certain versatility.