Długosz Jan, Dao Phong B, Staszewski Wiesław J, Uhl Tadeusz
Department of Robotics and Mechatronics, Faculty of Mechanical Engineering and Robotics, AGH University of Krakow, 30-059 Krakow, Poland.
Sensors (Basel). 2024 Mar 20;24(6):1980. doi: 10.3390/s24061980.
Hyperspectral imaging (HSI) is a remote sensing technique that has been successfully applied for the task of damage detection in glass fibre-reinforced plastic (GFRP) materials. Similarly to other vision-based detection methods, one of the drawbacks of HSI is its susceptibility to the lighting conditions during the imaging, which is a serious issue for gathering hyperspectral data in real-life scenarios. In this study, a data conditioning procedure is proposed for improving the results of damage detection with various classifiers. The developed procedure is based on the concept of signal stationarity and cointegration analysis, and achieves its goal by performing the detection and removal of the non-stationary trends in hyperspectral images caused by imperfect lighting. To evaluate the effectiveness of the proposed method, two damage detection tests have been performed on a damaged GFRP specimen: one using the proposed method, and one using an established damage detection workflow, based on the works of other authors. Application of the proposed procedure in the processing of a hyperspectral image of a damaged GFRP specimen resulted in significantly improved accuracy, sensitivity, and F-score, independently of the type of classifier used.
高光谱成像（HSI）是一种遥感技术，已成功应用于玻璃纤维增强塑料（GFRP）材料的损伤检测任务。与其他基于视觉的检测方法类似，HSI的缺点之一是其在成像过程中易受光照条件的影响，这在实际场景中采集高光谱数据时是一个严重问题。在本研究中，提出了一种数据预处理程序，以改进使用各种分类器进行损伤检测的结果。所开发的程序基于信号平稳性和协整分析的概念，并通过检测和去除由不完美照明引起的高光谱图像中的非平稳趋势来实现其目标。为了评估所提出方法的有效性，对一个受损的GFRP样本进行了两次损伤检测测试：一次使用所提出的方法，另一次使用基于其他作者工作的既定损伤检测工作流程。在所提出的程序应用于受损GFRP样本的高光谱图像的处理中，无论使用何种类型的分类器，都能显著提高准确性、灵敏度和F分数。
