Fabric defect detection using a hybrid particle swarm optimization-gravitational search algorithm and a Gabor filter.

Abstract

Recently, fabric defect inspection techniques have received attention in textile production procedures, since demands for various textile fabrics are growing. However, visual inspection for fabric defect detection is a very difficult problem because of the complexity of the fabric pattern and various defects. In this paper, we propose a method to detect the defects in fabric surfaces using the hybrid Particle Swarm Optimization-Gravitational Search Algorithm (PSO-GSA) and ellipse Gabor filter (EGF). In the proposed method, the hybrid PSO-GSA been employed to optimize the parameters of the EGF. Gabor filter parameters for the texture of the nondefective fabric images adjusted via the hybrid PSO-GSA with good convergence and solution characteristics. The defective fabric image is convoluted with the selected optimal Gabor filter, and we generate binary images by thresholding processing. The proposed method uses only one optimal filter, so fabric defect inspection is faster and more cost effective. Experimental results show that the proposed method is robust and achieves accurate detection of fabric defects.