Autonomous damage recognition in visual inspection of laminated composite structures using deep learning

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.

Automated summary

This study utilized deep learning techniques to quantitatively assess the visual detectability of different types of in-service damage in laminated composite structures. By creating an image dataset and utilizing various convolutional neural networks for classification, the study successfully identified the severity and types of damage with high accuracy. It was found that using AlexNet network with a relatively small image dataset provided the highest accuracy level compared to other models.