articles

At present, the automation of production processes, including the use of computer vision, machine learning and artificial intelligence methods, is of relevance at light industry enterprises due to the fourth industrial revolution. The key role in the production processes is played by the quality of manufactured products – textile fabrics, which is directly affected by the process of defectoscopy. Due to the development of digital technologies and the growth of computing power, it is possible to automate the process of defectoscopy of textile fabrics using computer vision to reduce labor costs and increase the accuracy of defect detection. The purpose of this paper is to conduct experimental studies of the marking and detection of specific classes of textile defects using a hardware-software complex of computer vision and using a neural network approach. To achieve this goal, the paper describes the existing classification of textile web defects, describes the used hardware-software system, and presents the application of the neural network model of the Mask R-CNN architecture to solve the problem of exemplar defect segmentation. As part of the study, a manual partitioning of more than 400 tissue photographs into two classes of defects was performed as an extension of the training sample: “weft crack” and “water damage”, the obtained results of the neural network model were evaluated by IoU metrics: the best result for the class “weft crack” DIoU = 0.2, for the class “water damage” DIoU = 0.87. Based on the results of the experimental studies, conclusions are made about the existing potential of using neural network approach for defectoscopy of similar classes of defects. The presented results can be used for training and retraining of various models of object detection, the gained experience can be applied in other spheres of industry.