Tracking yarns in high resolution fabric images: a real-time approach for online fabric flaw detection

An algorithmic framework for real-time localization of single yarns within industrial fabric images is presented. The information about precise yarn locations forms the foundation for a fabric flaw detection system which is based on individual yarn measurements. Matching a camera frame rate of 15 fps, we define the term “real-time” by the capability of tracking all yarns within a 5 megapixel image in less than 35 ms, leaving a time slot of 31ms for further image processing and defect detection algorithms. The processing pipeline comprises adaptive histogram equalization, Wiener deconvolution, normalized template matching and a novel feature point sorting scheme. To meet real-time requirements, extensive use of the NVIDIA CUDA framework is made. Implementation details are given and source code for selected algorithms is provided. Evaluation results show that wefts and warps can be tracked reliably and independently of the fabric material or binding. Video and image footage is provided on the project website to expand the paper content.