alexa Abstract | Automatic Defect Detection of Steel Products Using Supervised Classifier
ISSN ONLINE(2320-9801) PRINT (2320-9798)

International Journal of Innovative Research in Computer and Communication Engineering
Open Access

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Research Article Open Access


This paper presents an automatic defect identification system for detecting defects of steel products from captured digital radiographic images based on defect classification and segmentation. Image classification will be used for automated visual inspection to classify defect protects from quality one. It will be performed through textures analysis and probabilistic neural network. The textures are extracted using wavelet filters with co-occurrence features. The defect detection process involves the pre-processing, segmentation and morphological filtering to make processing system more flexible with accuracy. Manual inspection used to define the existence of defect becomes an urgent and important task in order to make sure the evaluation is accurate for radiographer to make decision. But, the manual inspection might give in consistent results especially when there are plentiful defects needs to be evaluated due to human factor. Automated defect inspection and evaluation system could assist radiographer to assess the properties of defect accurately. The pre-processing stage is used to improve the image quality by removing the noise if it contaminated in an image or smoothening an image to make better segmentation defect region using Gaussian filtering or top hat transformation. Here the segmentation process will be done based on clustering model and in that fuzzy c means clustering will be approached for effective partitioning a defect region from other parts. After this process, Morphological filtering will be used to smooth the segment the region by removing the back ground noise and false defects. Finally, the defects are extracted with better accuracy. The simulated results will be shown that the used algorithms for this process generate accurate detection of defects rather than previous methods and other clustering models.

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Author(s): Mr.Arun .M, Mrs.Prathipa.R,Mr.Krishna.P.S.G


Structural Steel,Steel Industry

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