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.

中文翻译：

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使用混合粒子群优化-引力搜索算法和Gabor滤波器的织物缺陷检测。

近年来，由于对各种织物的需求不断增长，织物缺陷检查技术已在纺织品生产过程中引起关注。但是，由于织物图案的复杂性和各种缺陷，目视检查织物缺陷是一个非常困难的问题。在本文中，我们提出了一种使用混合粒子群优化引力搜索算法（PSO-GSA）和椭圆Gabor滤波器（EGF）来检测织物表面缺陷的方法。在提出的方法中，采用了混合型PSO-GSA来优化EGF的参数。通过混合PSO-GSA调整的无缺陷织物图像纹理的Gabor滤波器参数具有良好的收敛性和求解特性。使用所选的最佳Gabor滤波器对有缺陷的织物图像进行卷积，然后通过阈值处理生成二进制图像。所提出的方法仅使用一个最佳滤波器，因此织物缺陷检查更快且更具成本效益。实验结果表明，所提方法具有较好的鲁棒性，能够准确检测出织物疵点。