Fabric defect detection based on GLCM and Gabor filter: A comparison

Fabric defect detection has been an active area of research since a long time and still a robust system is needed which can fulfill industrial requirements. A robust automatic fabric defect detection system (FDDS) would results in quality products and more revenues. Many different approaches and method have been tried to implement FDDS. Most of them are based on two approaches, one is statistical like gray level co-occurrence (GLCM) and other is transform based like Gabor filter. This paper presents a new scheme for automated FDDS implementation using GLCM and also compare it with Gabor filter approach. GLCM texture statistics are extracted and plotted against the inter-pixel distance of GLCM as signal graph. The non-defective fabric image information is compared with the test fabric image. In Gabor filter based approach, a bank of Gabor filter with different scales and orientations is generated and fabric images are filtered with convolution mask. The generated magnitude responses are compared for defect decision. In our implementation of both approaches in same environment, the GLCM approach produces higher defect detection accuracies than Gabor filter approach and more computationally efficient.