Image analysis for seam-puckering evaluation

Seam puckering is often considered an undesirable wrinkling appearance along a seamline, and is a problem that concerns fabric, sewing machine and sewing thread manufacturers. Until now, the standardized evaluation method for seam-puckering grading is still a visual-based, subjective method. This research project was aimed at developing a computer-vision system for automatic seam-puckering evaluation to improve the consistency and efficiency of grading. Fabric seam images were captured by a customized image acquisition system, and the seam images and the optimal image parameters, such as length and width, were determined according to the results of human inspection. The seamline was located with edge detection and Hough transform techniques. After rotating and cropping the image, the projection profile was then obtained and smoothed with the locally scatter-plot smoothing (LOESS) algorithm. Five characteristic features were extracted from the smoothed profile. Finally, an artificial neural network classifier was created to realize the automatic assessment of the seam-puckering grade. The experimental results proved that the proposed system can achieve accurate seam-puckering grades, and has the potential to replace the current manual evaluation.