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.

翻译：最近开始采用深层学习方法来解决工业质量控制中的表面缺陷检测问题,然而,由于需要大量数据来学习,往往需要高精度标签,许多工业问题无法轻易解决,或由于说明要求,解决方案的成本将大大增加。在这项工作中,我们放松对充分监督的学习方法的大量要求,并减少对高度详细说明的需要。通过提出深层学习结构,我们探索如何使用各种细节的说明,从薄弱(图像级别)标签到全面(像素级别)关于表面缺陷检测任务的全面(像素级别)说明。拟议的端对端结构由两个子网络组成,产生缺陷分解和分类结果。在工业质量检查的若干数据集上,即KolektorSDD、DGM和Severstal Steet Desfect。我们还提出了一个称为KolektorSDD2的新数据集,该数据集包含若干类型的缺陷,但在解决现实世界工业问题时获得的低度(像素水平)说明。我们提出的端对端对端结构结构结构结构结构的配置,也只是展示了一种完全监督的稳重的模型,在四个结构中展示了一种完全的模型。