Surface Defect Detection Using Texture Features and RBFN

Qiang Song

doi:10.4028/www.scientific.net/AMM.44-47.3529

Paper Titles

Guiding Simulation Model Verification by Model Checking
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Embedding the Importance of Commercialisation of University’s Research in the Engineering Education in Malaysia
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Data Fusion for MFL Signal Characterization
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Effects of Apparatus Parameters on MFL Signals Using Orthogonal Experimental Design
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Surface Defect Detection Using Texture Features and RBFN
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Real-Time 3D Space Coordinate Acquisition of Medical Visualization Data
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Inverse Classification Problem of Quantitative Attributes
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Research on Innovative Talents Training Mode Based on Off-Campus High-End Practice Environment
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The Static Structural Complexity Metrics for Large-Scale Software System
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 44-47Surface Defect Detection Using Texture Features...

Surface Defect Detection Using Texture Features and RBFN

Abstract:

This paper is concerned with the problem of automatic inspection of hot-rolled plate surface using machine vision. An intelligent surface defect detection paradigm based on texture analysis and neural network is presented. Texture features based on GLCM, Laws energy, and LBP are extracted from ROI in hot-rolled plate surface images. These features are integrated into a feature vector which uniquely differentiates the abnormal regions from normal surface. A radial basis function network is used for classification of ROI as normal or abnormal. Classification accuracies using the individual feature sets and the integrated features are compared. The results indicate that the integrated features improve the accuracy of detection. Empirical results show the integrated features from GLCM and LBP perform well in classifying the samples with the lowest classification error.