摘要
An effective method for automatic image inspection of fabric defects is presented. The proposed method relies on a tuned 2D-Gabor filter and quantum-behaved particle swarm optimization( QPSO) algorithm. The proposed method consists of two main steps:( 1) training and( 2) image inspection. In the image training process,the parameters of the 2D-Gabor filters can be tuned by QPSO algorithm to match with the texture features of a defect-free template. In the inspection process, each sample image under inspection is convoluted with the selected optimized Gabor filter.Then a simple thresholding scheme is applied to generating a binary segmented result. The performance of the proposed scheme is evaluated by using a standard fabric defects database from Cotton Incorporated. Good experimental results demonstrate the efficiency of proposed method. To further evaluate the performance of the proposed method,a real time test is performed based on an on-line defect detection system. The real time test results further demonstrate the effectiveness, stability and robustness of the proposed method,which is suitable for industrial production.
An effective method for automatic image inspection of fabric defects is presented. The proposed method relies on a tuned 2D-Gabor filter and quantum-behaved particle swarm optimization （QPSO） algorithm. The proposed method consists of two main steps： （1） training and （2） image inspection. In the image training process, the parameters of the 2D-Gabor filters can be tuned by QPSO algorithm to match with the texture features of a defect-free template. In the inspection process, each sample image under inspection is convoluted with the selected optimized Gabor filter. Then a simple thresholding scheme is applied to generating a binary segmented result. The performance of the proposed scheme is evaluated by using a standard fabric defects database from Cotton Incorporated. Good experimental results demonstrate the efficiency of proposed method. To further evaluate the performance of the proposed method, a real time test is performed based on an on-line defect detection system. The real time test results further demonstrate the effectiveness, stability and robustness of the proposed method, which is suitable for industrial production.
基金
the Innovation Fund Projects of Cooperation among Industries,Universities&Research Institutes of Jiangsu Province,China(Nos.BY2015019-11,BY2015019-20)
National Natural Science Foundation of China(No.51403080)
the Fundamental Research Funds for the Central Universities,China(No.JUSRP51404A)
the Priority Academic Program Development of Jiangsu Higher Education Institutions,China
