Automated Defect Detection of Steel Galvanizing Surface

Automatic defect detection of steel galvanizing surface is challenging due to its localization of defect , variation in shape, and their rare occurrences in the surface. The manual inspection might give inconsistent results especially when there are plentiful defects need to be evaluated due to human factor. But, An automatic defect identification system used to detect the defect correctly. It is based on defect classification. Image classification will be used for automated visual inspection. It will be performed through textures analysis and Probabilistic Neural Network. The textures are extracted using wavelet filters with co-occurrence features. This is exactly extract the structure by removing the back ground noise and the false defects. The Probabilistic Neural Network is used to differentiate the defect image from defect free one. Finally, the defects of the steel galvanizing surface are extracted with better accuracy.

کلیدواژه ها:

Texture Analysis ، Wavelet Filters ، Co-occurrence Features.