Development of a real-time machine vision system for functional textile fabric defect detection using a deep YOLOv4 model

We introduce a real-time machine vision system we developed with the aim of detecting defects in functional textile fabrics with good precision at relatively fast detection speeds to assist in textile industry quality control. The system consists of image acquisition hardware and image processing software. The software we developed uses data preprocessing techniques to break down raw images to smaller suitable sizes. Filtering is employed to denoise and enhance some features. To generalize and multiply the data to create robustness, we use data augmentation, which is followed by labeling where the defects in the images are labeled and tagged. Lastly, we utilize YOLOv4 for localization where the system is trained with weights of a pretrained model. Our software is deployed with the hardware that we designed to implement the detection system. The designed system shows strong performance in defect detection with precision of 95.3 % , and recall and F 1 scores of 93.6 % and 94.4 % , respectively. The detection speed is relatively fast at 34 fps with a prediction speed of 21.4 ms. Our system can automatically locate functional textile fabric defects with high confidence in real time.