Deep-learning methods have recently started being employed for addressing surface-defect detection problems in industrial quality control. However, with a large amount of data needed for learning, often requiring high-precision labels, many industrial problems cannot be easily solved, or the cost of the solutions would significantly increase due to the annotation requirements. In this work, we relax heavy requirements of fully supervised learning methods and reduce the need for highly detailed annotations. By proposing a deep-learning architecture, we explore the use of annotations of different details ranging from weak (image-level) labels through mixed supervision to full (pixel-level) annotations on the task of surface-defect detection. The proposed end-to-end architecture is composed of two sub-networks yielding defect segmentation and classification results. The proposed method is evaluated on several datasets for industrial quality inspection: KolektorSDD, DAGM and Severstal Steel Defect. We also present a new dataset termed KolektorSDD2 with over 3000 images containing several types of defects, obtained while addressing a real-world industrial problem. We demonstrate state-of-the-art results on all four datasets. The proposed method outperforms all related approaches in fully supervised settings and also outperforms weakly-supervised methods when only image-level labels are available. We also show that mixed supervision with only a handful of fully annotated samples added to weakly labelled training images can result in performance comparable to the fully supervised model's performance but at a significantly lower annotation cost.

深度学习方法最近已开始用于解决工业质量控制中的表面缺陷检测问题。但是，由于学习所需的大量数据（通常需要高精度的标签），许多工业问题无法轻易解决，或者由于注释要求，解决方案的成本将显着增加。在这项工作中，我们放宽了对完全受监督的学习方法的苛刻要求，并减少了对高度详细的注释的需求。通过提出一种深度学习的体系结构，我们探索了对不同细节的注释的使用，这些注释的范围从薄弱（图像级别）的标签到混合监督，再到表面缺陷检测任务的完整（像素级别的）注释。所提出的端到端体系结构由产生缺陷分割和分类结果的两个子网组成。在几种用于工业质量检查的数据集上评估了所提出的方法：KolektorSDD，DAGM和Severstal Steel Defect。我们还提出了一个名为KolektorSDD2的新数据集，其中包含3000幅包含几种缺陷类型的图像，这些图像是在解决现实世界中的工业问题时获得的。我们在所有四个数据集上展示了最新的结果。在仅图像级标签可用的情况下，建议的方法在完全监督的环境中优于所有相关方法，并且在弱监督的环境中也优于其他方法。