A Coupling Enhancement Algorithm for ZrO2 Ceramic Bearing Ball Surface Defect Detection Based on Cartoon-texture Decomposition Model and Multi-Scale Filtering Method

TL;DR

This paper presents a novel defect detection method for ZrO2 ceramic bearing balls using cartoon-texture decomposition and multi-scale filtering, significantly improving detection accuracy and efficiency.

Contribution

It introduces a surface defect detection system based on cartoon-texture decomposition combined with Gaussian curvature and multi-scale filtering, enhancing image denoising and defect detection accuracy.

Findings

Detection accuracy of 95.8% achieved

PSNR of 34.1 dB indicating high image quality

Detection speed of 191 ms per image

Abstract

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.