应用最优Gabor滤波器的经编织物疵点检测

针对经编织物疵点自动检测问题,提出了一种新的基于最优Gabor滤波器的经编织物疵点检测方法。具体可分为学习阶段和检测阶段;在学习阶段,对于无疵点的经编织物图像构造可调制的二维Gabor滤波器,采用量子行为粒子群优化(QPSO)算法对Gabor滤波器的参数进行优化,得到与无疵点的织物图像纹理特征最匹配的Gabor滤波器参数;在检测阶段,由学习阶段得到的最佳参数构造Gabor滤波器,用该滤波器对待检测织物图像进行卷积处理,然后再对得到的卷积图像进行二值化处理,最终识别出待检测织物是否有疵点存在。结果表明,该方法的检测率可以达到96.67%,具有很好的稳定性和鲁棒性,适合应用于工业生产。

Warp knit fabric defect detection method based on optimal Gabor filter

Focusing on automatic image inspection of warp knit fabric defects in textile industry, a new method for warp knit fabric defect detection based on an optimal Gabor filter is presented. The proposed method consists of two processes:the training process and the inspection process. In the training process, the parameters of the 2?D Gabor filter can be tuned by the quantum?behaved particle swarm optimization ( QPSO) algorithm to match with the texture features of a defect?free template acquired in prior. In the inspection process, each sample fabric image under inspection is convoluted with the selected optimized Gabor filter. Then a simple thresholding scheme is applied to generate a binary segmented result. Experimental results show that the detection rate of the proposed method can reach 96?67%. It has good performance of stability and robustness, suitable for industrial production.