Surface Roughness Detection Based on Texture Analysis
基于纹理分析的表面粗糙度等级识别

With the growing emphasis of industrial automation in manufact uring, vision techniques play an important role in many applications. Since diff erent surfaces have different textures, the techniques of texture analysis can b e used for the recognition of surfaces. In this paper, a novel non-contacted ap proach to measure the roughness of machined surfaces based on texture analysis t echniques is presented. When using Gabor filters, It is more complex to classify multiple textural images than to distinguish the texture between two images. Ac cording to other related paper and our experiments, the surface of a measured sp ecimen can be classified coarsely according to its gray-level variance. Then, t he roughness of the surface can be detected using Gabor filters. We present the method of designing the filters and the experiments show better results as well. The approach can detect the surface roughness automatically and quickly. It is invariant to rotation, and has fewer classifiers. Furthermore the cost of the de vice for implementing the approach is low and the parameters can be set easily. If the system is connected with the control system of a machine, we can realize real-time close looped control of the machining procedure.