Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Kneza Trpimira 2b, Osijek, 31000, Croatia.
ISA Trans. 2022 Jun;125:400-414. doi: 10.1016/j.isatra.2021.06.025. Epub 2021 Jun 19.
Automated defect detection is difficult to achieve in ceramic tile manufacturing today. Computer vision and machine learning based approaches are commonly utilised for this purpose. This paper considers the problem of defect detection in the textured ceramic tiles quality analysis. Instead of detecting defects on the finished tile, the biscuit tile is considered, a pressed, dried, decorated tile before its firing in the kiln. As it is an intermediary product during tile production, classifying them as defected or not before the firing can significantly reduce energy and material costs. To this end, in this paper we propose a new Fourier spectrum annuli feature extraction method. It is based on Fourier spectrum of the surface biscuit tile image and tested on real tile examples from the ceramic tile industry. According to the observed results, it outperforms several well-known methods for feature extraction on real-world tile datasets reaching an F1 score of 0.9236 and 0.8866 on the Black Random Stripes and Stripes Brown Light tile designs respectively.
如今，在瓷砖制造中实现自动化缺陷检测很困难。计算机视觉和基于机器学习的方法通常用于实现这一目标。本文考虑了纹理瓷砖质量分析中的缺陷检测问题。本文不是在成品瓷砖上检测缺陷，而是考虑了在烧制之前压制、干燥、装饰的生坯瓷砖。由于它是瓷砖生产过程中的中间产品，因此在烧制之前将它们分类为有缺陷或无缺陷可以显著降低能源和材料成本。为此，本文提出了一种新的傅里叶谱圆环特征提取方法。它基于表面生坯瓷砖图像的傅里叶谱，并在瓷砖行业的真实瓷砖示例上进行了测试。根据观察到的结果，它在真实瓷砖数据集上的特征提取方面优于几种知名方法，在黑色随机条纹和条纹棕光瓷砖设计上的 F1 分数分别达到 0.9236 和 0.8866。
