An approach to defect detection in materials characterized by complex textures
Pattern Recognition
Real-time vision-based system for textile fabric inspection
Real-Time Imaging
Texture segmentation using wavelet transform
Pattern Recognition Letters
Expert Systems with Applications: An International Journal
Fabric defect detection based on multiple fractal features and support vector data description
Engineering Applications of Artificial Intelligence
Hi-index | 0.00 |
Though copper products are important raw materials in industrial production, there is little domestic research focused on copper strip surface defects inspection based on automated visual inspection. According to the defect image characteristics on copper strips surface, a defect detection algorithm is proposed on the basis of wavelet-based multivariate statistical approach. First, the image is divided into several sub-images, and then each sub-image is further decomposed into multiple wavelet processing units. Then each wavelet processing unit is decomposed by 1-D db4 wavelet function. Then multivariate statistics of Hotelling T2 are applied to detect the defects and SVM is used as defect classifier. Finally, the defect detection performance of the proposed approach is compared with traditional method based on grayscale. Experimental results show that the proposed method has better performance on identification, especially its application in the ripple defects can achieve 96.7% accuracy, which was poor in common algorithms.