论文标题
ZRO2陶瓷轴承球表面缺陷检测的耦合增强算法基于卡通纹理分解模型和多尺度滤波方法
A Coupling Enhancement Algorithm for ZrO2 Ceramic Bearing Ball Surface Defect Detection Based on Cartoon-texture Decomposition Model and Multi-Scale Filtering Method
论文作者
论文摘要
这项研究旨在提高ZRO2陶瓷轴承球的表面缺陷检测准确性。结合图像样品的噪声损伤，提出了一种基于卡通纹理分解模型的ZRO2陶瓷轴承球的表面缺陷检测方法。构建ZRO2陶瓷轴承球表面缺陷检测系统。通过使用高斯曲率模型分解ZRO2陶瓷轴承球表面缺陷图像是分解的，并使用赢家过滤器和小波值域滤波器过滤分解的图像层。然后将它们融合到清晰且未损坏的ZRO2陶瓷轴承球表面缺陷图像中并检测到。实验结果表明，基于卡通纹理分解模型的ZRO2陶瓷轴承球表面缺陷的图像降解方法可以在保留图像细节的同时降级。图像的PSNR为34.1 dB，SSIM为0.9476，检测准确性为95.8％，单个缺陷图像的检测速度为191ms / img。该方法可以有效提高ZRO2陶瓷轴承球表面缺陷检测的效率和准确性。
This study aimed to improve the surface defect detection accuracy of ZrO2 ceramic bearing balls. Combined with the noise damage of the image samples, a surface defect detection method for ZrO2 ceramic bearing balls based on cartoon-texture decomposition model was proposed. Building a ZrO2 ceramic bearing ball surface defect detection system. The ZrO2 ceramic bearing ball surface defect image was decomposed by using the Gaussian curvature model and the decomposed image layer was filtered by using Winner filter and wavelet value domain filter. Then they were fused into a clear and undamaged ZrO2 ceramic bearing ball surface defect image and detected. The experimental results show that the image denoising method of ZrO2 ceramic bearing ball surface defect based on cartoon-texture decomposition model can denoise while retaining the image details. The PSNR of image is 34.1 dB, the SSIM is 0.9476, the detection accuracy is 95.8%, and the detection speed of a single defect image is 191ms / img. This method can effectively improve the efficiency and accuracy of ZrO2 ceramic bearing ball surface defect detection.
