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Segmentation-based deep-learning approach for surface-defect detection
Journal of Intelligent Manufacturing ( IF 8.3 ) Pub Date : 2019-05-15 , DOI: 10.1007/s10845-019-01476-x
Domen Tabernik , Samo Šela , Jure Skvarč , Danijel Skočaj

Abstract

Automated surface-anomaly detection using machine learning has become an interesting and promising area of research, with a very high and direct impact on the application domain of visual inspection. Deep-learning methods have become the most suitable approaches for this task. They allow the inspection system to learn to detect the surface anomaly by simply showing it a number of exemplar images. This paper presents a segmentation-based deep-learning architecture that is designed for the detection and segmentation of surface anomalies and is demonstrated on a specific domain of surface-crack detection. The design of the architecture enables the model to be trained using a small number of samples, which is an important requirement for practical applications. The proposed model is compared with the related deep-learning methods, including the state-of-the-art commercial software, showing that the proposed approach outperforms the related methods on the specific domain of surface-crack detection. The large number of experiments also shed light on the required precision of the annotation, the number of required training samples and on the required computational cost. Experiments are performed on a newly created dataset based on a real-world quality control case and demonstrates that the proposed approach is able to learn on a small number of defected surfaces, using only approximately 25–30 defective training samples, instead of hundreds or thousands, which is usually the case in deep-learning applications. This makes the deep-learning method practical for use in industry where the number of available defective samples is limited. The dataset is also made publicly available to encourage the development and evaluation of new methods for surface-defect detection.



中文翻译:

基于分段的深度学习方法进行表面缺陷检测

摘要

使用机器学习的自动表面异常检测已成为有趣且有前途的研究领域,对视觉检测的应用领域具有非常高的直接影响。深度学习方法已成为完成此任务的最合适方法。它们使检查系统可以通过简单地显示一些示例图像来学习检测表面异常。本文提出了一种基于分割的深度学习体系结构,该体系结构旨在检测和分割表面异常,并在表面裂纹检测的特定领域进行了演示。架构的设计使得可以使用少量样本来训练模型,这是实际应用的重要要求。将该模型与相关的深度学习方法进行了比较,包括最先进的商业软件,表明该方法在表面裂纹检测的特定领域优于相关方法。大量的实验还阐明了注释所需的精度,所需训练样本的数量以及所需的计算成本。在真实世界质量控制案例的基础上,对新创建的数据集进行了实验,并证明了所提出的方法仅使用大约25–30个有缺陷的训练样本,而不是数百或数千个缺陷样本,即可在少量缺陷表面上进行学习,这在深度学习应用程序中通常是这种情况。这使得深度学习方法可实际用于有限缺陷样本数量有限的工业中。

更新日期:2020-03-04
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