基于深度学习的纹理布匹瑕疵检测方法

布匹瑕疵检测是纺织工业中产品质量评估的关键环节,实现快速、准确、高效的布匹瑕疵检测对于提升纺织工业的产能具有重要意义.在实际布匹生产过程中,布匹瑕疵在形状、大小及数量分布上存在不平衡问题,且纹理布匹复杂的纹理信息会掩盖瑕疵的特征,加大布匹瑕疵检测难度.本文提出基于深度卷积神经网络的分类不平衡纹理布匹瑕疵检测方法(Detecting defects in imbalanced texture fabric based on deep convolutional neural network, ITF-DCNN),首先建立一种基于通道叠加的ResNet50卷积神经网络模型(ResNet50+)对布匹瑕疵特征进行优化提取;其次提出一种冗余特征过滤的特征金字塔网络(Filter-feature pyramid network, F-FPN)对特征图中的背景特征进行过滤,增强其中瑕疵特征的语义信息;最后构造针对瑕疵数量进行加权的MFL (Multi focal loss)损失函数,减轻数据集不平衡对模型的影响,降低模型对于少数类瑕疵的不敏感性.通过实验对比,提出的方法能有效提升布匹瑕疵检测的准确...

Detection of Detecting Textured Fabric Defects Based on Deep Learning

Fabric defect detection is a key part of product quality assessment in the textile industry. Achieving fast, accurate and efficient fabric defect detection is of great significance for improving the productivity of the textile industry. In the production process of fabric, imbalance exists in the shape, size and quantity distribution of fabric defects, and the complex texture information of the jacquard fabric will cover the characteristics of the defect, which makes it difficult to detect fabric defects. This paper proposes a method for detecting defects in imbalanced texture fabric based on deep convolutional neural network (ITF-DCNN). First, an improved ResNet50 convolutional neural network model (ResNet50+) based on channel concatenate is established to optimize the fabric defect features. Second, F-FPN (filter-feature pyramid network) method for filtering redundant feature is proposed to filter the background features in the feature maps and enhance the semantic information of defect features. Finally, a MFL (multi focal loss) function weighted with the number of defects is construct to reduce the impact of imbalance on the model, and reduce the model＇s insensitivity to a small number of defects. Experiments shows the proposed method effectively improves the accuracy of fabric defect detection and the accuracy of defect positioning, while reducing the false detection rate and missed detection rate of defect detection, which is significantly higher than the mainstream fabric defect detection algorithm.