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 $95.3\mathrm{\%}$, and recall and $F1$ scores of $93.6\mathrm{\%}$ and $94.4\mathrm{\%}$, 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.

我们引入了我们开发的实时机器视觉系统，旨在以相对较快的检测速度以良好的精度检测功能性纺织面料中的缺陷，以协助纺织行业的质量控制。该系统由图像采集硬件和图像处理软件组成。我们开发的软件使用数据预处理技术将原始图像分解为更小的合适尺寸。滤波用于去噪和增强某些特征。为了对数据进行泛化和乘法以创建稳健性，我们使用数据增强，然后在标记和标记图像中的缺陷处进行标记。最后，我们利用 YOLOv4 进行定位，其中系统使用预训练模型的权重进行训练。我们的软件与我们为实现检测系统而设计的硬件一起部署。$95.3\mathrm{\%}$，并回忆和 $F1$ 分数 $93.6\mathrm{\%}$ 和 $94.4\mathrm{\%}$， 分别。检测速度相对较快$34$ fps，预测速度为 $21.4$多发性硬化症。我们的系统可以实时、高度自信地自动定位功能性纺织面料缺陷。