Abstract:

Focusingonautomaticimageinspectionofwarpknitfabricdefectsintextileindustry,anewmethodforwarpknitfabric defect detection based on an optimal Gabor filter is presented. The proposed method consists of two process: the training and the inspection process. In the training process, the parameters of the 2D-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.

Key words: warp knit fabric defect detection, optimal Gabor filter, quantum-behaved particle swarm optimization algorithm, image segmentation