摘要：

带钢表面质量是衡量钢铁产品质量的重要指标之一, 针对全流程表面缺陷进行分类研究, 可以减少表面缺陷的发生, 同时提升表面缺陷信息捕获的准确性。在实际生产过程中, 带钢缺陷样本的精准类别标签往往难以获取, 因此不依赖标签数据的无监督分类方法逐渐成为研究热点。现有的传统机器学习无监督分类方法对噪声数据鲁棒性差, 而基于深度学习的无监督方法对数据量依赖性较强。为此, 将传统的机器学习算法和深度学习算法相结合, 提出一种无监督动态加权联合的带钢表面缺陷分类(DWJC)方法。首先, 根据纹理特征聚类算法为缺陷图像分配初始类别标签; 然后, 通过卷积神经网络(CNN)提取图像的深度特征; 最后, 基于KL散度提出一种动态加权重标注方法, 联合初始类别标签、Softmax、约束聚类等多个分类方法, 在模型训练过程中不断修正初始类别标签, 以获取更加稳定且精准的缺陷分类结果。在NEU公共数据集和上海宝钢缺陷数据集上进行大量实验, 结果表明, DWJC分别取得了99.5%和94.3%的平均精度。

关键词: 表面缺陷分类, 无监督分类, 纹理特征, 聚类算法, 动态权重

Abstract:

The surface quality of strip steel is an important indicator of the quality of steel products. Research on the classification of surface defects throughout the production process can reduce the occurrence of surface defects and improve the accuracy of capturing surface defect information. In the actual production process, obtaining accurate category labels for steel strip defect samples is often difficult. Therefore, unsupervised classification methods that do not rely on labeled data have gradually become a research hotspot. Existing traditional machine learning-based unsupervised classification methods are not robust against noisy data, whereas deep learning-based unsupervised methods depend on data volume. This study combines traditional machine learning and deep learning algorithms to propose an unsupervised Dynamic Weight Joint Classification (DWJC) method for surface defects in steel strips. First, the initial category labels of defect images are obtained using the texture feature clustering algorithm; then, the depth features of the image are extracted through a Convolutional Neural Network (CNN). This study also proposes a dynamic weighted re-labeling method based on KL divergence, which combines initial class labels, Softmax, and constraint clustering to continuously modify the initial class labels during model training, to obtain more stable and accurate defect classification results. In a large number of experiments on the NEU public and Baosteel defect datasets, DWJC achieves average accuracies of 99.5% and 94.3%, respectively.

Key words: surface defect classification, unsupervised classification, texture features, clustering algorithm, dynamic weight