SURFACE ROUGHNESS DETECTION OF INNER WALL OF PIPBASED ON SVM AND VARIANCE

In this paper, an novel non-contacted approach is presented to measure the inner wall roughness of pipe. The approach is based on gray-level variance and Support Vector Machine (SVM) which considers a candidate to replace neural networks and other traditional classification methods for its good performance. But the time consumption of SVM is vast when the input data is vast. A referance is made to other related papers and our experiments, the surface of a measured specimen can be classified coarsely according to its gray-level variance. Then the surface roughness can be detected by SVM. The approach takes advantages of both SVM and variant. The experiments show that the approach is accurate and efficient. Furthermore, it can be used widely in detecting the surface roughness of other objects by adjusting some parameters rightly.