Damage detection of composite laminates based on deep learnings - Jiang, JianHua; Wang, Zhengshui (Frontiers in Physics, 2024)
Composite structure is widely used in various technological fields because of its superior material properties. Composite structure detection technology has been exploring efficient and fast damage detection technology. In this paper, image-based NDT technology is proposed to detect composite damage using deep learning. A data set was established through literature, which contained images of damaged and non-damaged composite material structures. Then, five convolutional neural network models Alexnet, VGG16, ResNet-34, ResNet-50 and GoogleNet were used to automatically classify the damage. Finally, the performance of five pre-trained network architectures is evaluated, and the results show that RESNET-50 technology can successfully detect damage in a reasonable computation time with the highest accuracy and low complexity using relatively small image datasets.