Due to the deforming and dynamically changing textile fibers, the quality assurance of cleaned industrial textiles is still a mostly manual task. Usually, textiles need to be spread flat, in order to detect defects using computer vision inspection methods. Already known methods for detecting defects on such inhomogeneous, voluminous surfaces use mainly supervised methods based on deep neural networks and require lots of labeled training data. In contrast, we present a novel unsupervised method, based on SURF keypoints, that does not require any training data. We propose using their location, number and orientation in order to group them into geographically close clusters. Keypoint clusters also indicate the exact position of the defect at the same time. We furthermore compared our approach to supervised methods using deep learning. The presented processing pipeline shows how normalization and classification methods need to be combined, in order to reliably detect fiber defects such as cuts and holes. We evaluate the performance of our system in real-world settings with images of piles of textiles, taken in stereo vision. Our results show that our novel unsupervised classification method using keypoint clustering achieves comparable results to other supervised methods.

中文翻译：

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使用关键点和深度学习检测光纤缺陷

由于纺织纤维的变形和动态变化，清洁工业纺织品的质量保证仍然主要是一项手动任务。通常，纺织品需要平铺，以便使用计算机视觉检测方法检测缺陷。用于检测此类不均匀、大量表面上的缺陷的已知方法主要使用基于深度神经网络的监督方法，并且需要大量标记的训练数据。相比之下，我们提出了一种基于 SURF 关键点的新型无监督方法，它不需要任何训练数据。我们建议使用它们的位置、数量和方向来将它们分组到地理上接近的集群中。关键点簇还同时指示缺陷的确切位置。我们进一步将我们的方法与使用深度学习的监督方法进行了比较。所提出的处理流程显示了需要如何结合归一化和分类方法，以便可靠地检测光纤缺陷，例如切口和孔洞。我们使用立体视觉拍摄的成堆纺织品图像来评估我们系统在现实环境中的性能。我们的结果表明，我们使用关键点聚类的新型无监督分类方法取得了与其他监督方法相当的结果。