Autonomous damage recognition in visual inspection of laminated composite structures using deep learning
Autonomous damage recognition in visual inspection of laminated composite structures using deep learning
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DOI：
10.1016/j.compstruct.2021.113960
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发表时间：
2021-04-24
影响因子：
6.3
通讯作者：
Fotouhi, Mohamad
中科院分区：
文献类型：
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作者：
Fotouhi, Sakineh;Pashmforoush, Farzad;Fotouhi, Mohamad
本研究提出了利用深度学习定量评估层压复合材料结构（如飞机和风力涡轮机叶片）中不同类型在役损伤的视觉可检测性。一个全面的基于图像的数据集收集从文献中包含常见的微观损伤机制（基体开裂和纤维断裂）和宏观损伤机制（影响和侵蚀）。然后，通过AlexNet的预训练版本对损坏类型和严重程度进行自动分类，AlexNet是一种用于图像处理的稳定卷积神经网络。预训练的ResNet-50和其他5个用户定义的卷积神经网络也被用来评估AlexNet的性能。结果表明，采用AlexNet网络，使用相对较小的图像数据集，在合理的计算时间内提供了最高的准确率水平（87%-96%），用于识别损伤的严重程度和类型。本文中生成的知识和收集的图像数据将促进复合材料结构自主视觉检测领域的进一步研究和开发，有可能显着降低成本，健康和安全风险以及与完整性评估相关的停机时间。
This study proposes the exploitation of deep learning for quantitative assessment of visual detectability of different types of in-service damage in laminated composite structures such as aircraft and wind turbine blades. A comprehensive image-based data set is collected from the literature containing common microscale damage mechanisms (matrix cracking and fibre breakage) and macroscale damage mechanisms (impact and erosion). Then, automated classification of the damage type and severity was done by pre-trained version of AlexNet that is a stable convolutional neural network for image processing. Pre-trained ResNet-50 and 5 other user-defined convolutional neural networks were also used to evaluate the performance of AlexNet. The results demonstrated that employing AlexNet network, using the relatively small image dataset, provided the highest accuracy level (87%-96%) for identifying the damage severity and types in a reasonable computational time. The generated knowledge and the collected image data in this paper will facilitate further research and development in the field of autonomous visual inspection of composite structures with the potential to significantly reduce the costs, health & safety risks and downtime associated with integrity assessment.