Development and evaluation of a color-image-based visual roughness measurement method with illumination robustness

Abstract

At present, the application of machine vision methods for roughness measurement in production sites is limited by its adaptability to illumination variations during the measurement. In this study, a machine vision method for roughness measurement with robustness to illumination is proposed so as to explore the functions of its color image indices in improving the mathematical expression of the vector of three primary colors. Besides, virtual images of different-roughness surfaces were analyzed, the effects of the samples’ surface texture orientations on measurement indices were discussed, and the singular value ratio was derived as an index for evaluating roughness. The experimental results showed that the samples’ index values remained unchanged when the illumination was increased for both vertical and horizontal surface textures, indicating that the proposed method has strong robustness to illumination. In addition, the experimental results were verified by a support vector machine (SVM)-based method using 10 different-roughness test samples, with the verification range of 0.127–2.245 µm. It was found that the measurement accuracy reached 90%, suggesting that the proposed method is reasonable and feasible, and shows certain potential to be applied in engineering.

A visual measurement method of grinding surface roughness based on aliasing region index and neural network

“…Zhang et al [18] used inductive transfer learning and a limited number of standard training samples to build roughness measurement model. Zhao et al [19] explored the functionality of color image indices in improving the mathematical representation of three primary color vectors, and derived the singular value ratio for roughness. The above methods have effectively evaluated surface roughness through feature extraction.…”

Illumination-robust milling surface roughness machine vision inspection based on MAML++ network

“…Patil and Kulkarni 8 proposed a roughness inspection method based on the singular value decomposition utilizing objective speckle patterns of the machined surface. Zhao et al 9 put forward a roughness inspection method with illumination robustness by combining the three primary colors (R, G, and B) of color images with quaternions. With respect to the index-free design, Chen et al 10 used a deep convolutional neural network to classify surface roughness and achieved roughness detection with an index-free design.…”

“…Patil and Kulkarni 8 proposed a roughness inspection method based on the singular value decomposition utilizing objective speckle patterns of the machined surface. Zhao et al 9 . put forward a roughness inspection method with illumination robustness by combining the three primary colors (R, G, and B) of color images with quaternions.…”

3D Roughness Prediction Modeling and Evaluation of Textured Liner of Piston Component-cylinder System

“…R. S. Umamaheswara Raju et al [24] used curvelet transform to obtain the characteristic parameters of the gray image of surface topography, which realized the evaluation of 2D roughness topography. The above works [17][18][19][20][21][22][23][24] did not consider the influence of the anti-friction texture on the surface when reconstruct the reference plane of surface gray image.…”

“…U. Zuperl et al [19] proposed a roughness prediction model to achieve grinding roughness by genetic algorithms (GA), artificial neural network and adaptive neural fuzzy algorithm (ANFA). The size of the virtual image area formed by color light sources on different rough surfaces is considered, and a correlation model between color image sharpness index and roughness is constructed to evaluate the roughness [20][21][22][23]. R. S. Umamaheswara Raju et al [24] used curvelet transform to obtain the characteristic parameters of the gray image of surface topography, which realized the evaluation of 2D roughness topography.…”