Automatic inspection methods based on machine vision have been widely employed for steel surface defect detection. The central purpose of these methods is to extract features to represent different defects. However, current methods depend on machine learning that demands handcrafted features and overlooks the domain shift. In this paper, we propose a new method combining domain adaptation (DA) and adaptive convolutional neural network (ACNN), called DA-ACNN, to achieve steel surface defect detection. The convolutional neural network (CNN) is used as the backbone. To account for the lack of labels in a new domain, we introduce an additional domain classifier and a constraint on label probability distribution to achieve the cross-domain and cross-task recognition. The normal distribution and the quadratic function are used to optimize the loss to improve the network performance. Adaptive learning rates based on the loss and the weight, respectively, are proposed to minimize the losses of DA and classification. We conducted experiments on steel surface defect datasets to validate the effectiveness of DA-ACNN. Compared with the classical CNN and other approaches, the results demonstrate the superiority of the proposed method.

基于机器视觉的自动检查方法已广泛用于钢表面缺陷检测。这些方法的主要目的是提取特征以表示不同的缺陷。但是，当前的方法依赖于需要手工功能且忽略域转换的机器学习。在本文中，我们提出了一种结合域自适应（DA）和自适应卷积神经网络（ACNN）的新方法，称为DA-ACNN，以实现钢表面缺陷检测。卷积神经网络（CNN）被用作主干。为了解决新域中缺少标签的问题，我们引入了一个额外的域分类器以及对标签概率分布的限制，以实现跨域和跨任务的识别。正态分布和二次函数用于优化损耗，以提高网络性能。提出了分别基于损失和权重的自适应学习率，以最小化DA和分类的损失。我们对钢表面缺陷数据集进行了实验，以验证DA-ACNN的有效性。与经典的CNN和其他方法相比，结果证明了该方法的优越性。