College of Chemical and Biological Engineering, Zhejiang University, 310058, Hangzhou, Zhejiang Province, China; Institute of Zhejiang University-Quzhou, 324000, Quzhou, Zhejiang Province, China.
College of Chemical and Biological Engineering, Zhejiang University, 310058, Hangzhou, Zhejiang Province, China; Institute of Zhejiang University-Quzhou, 324000, Quzhou, Zhejiang Province, China; Department of Computer Science, University of Reading, RG6 6AH, Reading, Berkshire, UK.
ISA Trans. 2023 Jun;137:706-716. doi: 10.1016/j.isatra.2023.01.009. Epub 2023 Jan 9.
Crystalline polymer powder inevitably incorporates certain impurities, including decomposed polymers or foreign particles. An essential criterion for assessing the quality of polymers is the quantity of contaminants in the powder. However, it is challenging to discern powder contaminants through machine vision due to the poor quality of images taken at production sites. Inspired by the spectral properties of crystalline polymers, this paper proposes an efficient image-based impurity detection method, which seeks to precisely and robustly detect contaminant content. Based on the changes in absorbance during polymer decomposition, a highly selective channel-weighted image enhancement approach is designed to emphasize the difference between impurities and normal particles. Then, using the prior information on the powder's attributes, an adaptive thresholding method is employed to categorize pixels belonging to impurities. Finally, a dataset of 119 12-megapixel photos from a chemical facility, where the average size of contaminants in images is 43 pixels, is used to evaluate the performance of the proposed algorithm. The results of the detection demonstrate that the proposed strategy for image enhancement has better selectivity to impurities than typical image enhancement methods.
结晶聚合物粉末不可避免地会包含一定的杂质，包括分解聚合物或外来颗粒。评估聚合物质量的一个基本标准是粉末中污染物的数量。然而，由于生产现场拍摄的图像质量较差，通过机器视觉来辨别粉末污染物具有挑战性。受结晶聚合物光谱特性的启发，本文提出了一种高效的基于图像的杂质检测方法，旨在精确和稳健地检测污染物含量。基于聚合物分解过程中吸光度的变化，设计了一种高度选择性的通道加权图像增强方法，以强调杂质和正常颗粒之间的差异。然后，利用粉末属性的先验信息，采用自适应阈值方法对属于杂质的像素进行分类。最后，使用来自化学工厂的 119 张 1200 万像素的照片数据集对所提出算法的性能进行评估，图像中污染物的平均尺寸为 43 像素。检测结果表明，与典型的图像增强方法相比，所提出的图像增强策略对杂质具有更好的选择性。
