Abstract: China’s current textile and clothing exports exceed one-third of the world’s proportion, ranking first in the world, so the development of textile enterprises is particularly important. But in the process of production, textiles are easily affected by environmental factors, equipment defects, human errors, poor technology, shortage of raw materials and other problems, resulting in skips, holes, water stains and other defects that affect product quality, and at present, as for the fabric defect detection link of most enterprises, manual visual inspection method with high cost and low efficiency is adopted, which causes worker fatigue and is easy to produce missed detection and false detection. Therefore, the research on intelligent fabric defect detection technology can effectively improve the production efficiency of textile enterprises, improve detection accuracy, and reduce production costs.
Compared with the traditional defect detection algorithm applied to fabrics, the detection method based on deep learning has better adaptability and learning. To solve the problems of low accuracy, high missed detection rate, slow training speed and difficult convergence of the model in traditional enterprise applications, based on the YOLOv7 algorithm with the latest Extended-ELAN architecture, the DCCSPC parallel stacking module was designed by using the cavity convolution with different parameter values, the SPPCSC spatial pyramid pooling layer was improved, and the local and overall characteristic information of fabric defects was deeply integrated. The top-down feature extraction network of the model was selected from the bottom of the feature extraction network, the feature matrix generated by the first ELAN module was spliced after 2-fold upsampling, a higher predicted feature map of 160×160×255 size was output, and the small defects of fabrics with a width and height of only more than four pixels were predicted. To solve the problem of slow convergence caused by increasing the number of model parameters, the CIoU loss function was replaced with the WIoU loss function, solving the problem of high missed detection rate of special samples (samples with aspect ratios inconsistent with most samples), and improving the convergence speed of the model. On this basis, 2,438 fabric defect image production datasets from the platform of Alibaba Tianchi were selected, and through ablation tests and comparative experiments with other detection algorithms, it was shown that the improved model could effectively detect hair defects with a lower proportion of pixels, predict defects such as dead wrinkles in thin strips, improve the recognition rate of water stains and other color and background defects, and increase the average accuracy value by 3.4%, which can meet the conditions of industrial-grade deployment and real-time detection, and be more effectively applied to intelligent defect detection in industrial production.    
Through the specific characteristics of fabric defect types, the YOLOv7 algorithm based on deep learning is used to improve the detection accuracy of specific defects and small defects, which provides an effective method for the detection of defects in intelligent production of textile enterprises, improves the detection quality, and provides an effective idea for further research on the defect detection algorithm.

Key words: YOLOv7, fabric defects, deep learning, object detection, dilated convolution

摘要： 针对企业目前应用的织物疵点检测算法精度低、漏检率高、训练速度慢及模型难以收敛的问题，提出了一种基于并联堆叠的织物疵点检测算法。基于YOLOv7深度学习目标检测算法，采用不同参数值的空洞卷积设计DCCSPC并联堆叠模块，改进SPPCSPC空间金字塔池化层，深度融合织物瑕疵局部及整体的特征信息；添加输出160×160的小目标检测层，实现宽高仅占4个像素的小目标疵点检测；使用WIoU损失函数替换CIoU损失函数，解决了特殊样本(长宽比与多数样本不一致的样本)漏检率高问题，并提高了模型的收敛速度。另外，为消除数据集类别不均衡问题，采用生成对抗网络对数量较少类别进行了样本扩充。将改进后算法在织物疵点数据集上进行测试，试验结果表明：与以往方法相比，改进后的模型具有更高的识别精度和鲁棒性，能够检出宽高比例悬殊、纹理对比度低图像中的疵点及小目标疵点，对比原YOLOv7模型，平均精度值提升3.4%，且收敛速度更快、误差更小，能够更好地满足当下织物智能疵点检测的需求。

关键词: YOLOv7, 织物疵点, 深度学习, 目标检测, 空洞卷积