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• W4200419654 abstract "Ultrasonic non-destructive testing can effectively detect damage in aircraft composite materials, but traditional manual testing is time-consuming and labor-intensive. To realize the intelligent recognition of aircraft composite material damage, this paper proposes a 1D-YOLO network, in which intelligent fusion recognizes both the ultrasonic C-scan image and ultrasonic A-scan signal of composite material damage. Through training and testing the composite material damage data on aircraft skin, the accuracy of the model is 94.5%, the mean average precision is 80.0%, and the kappa value is 97.5%. The use of dilated convolution and a recursive feature pyramid effectively improves the feature extraction ability of the model. The effectively used Cascade R-CNN (Cascade Region-Convolutional Neural Network) improves the recognition effect of the model, and the effectively used one-dimensional convolutional neural network excludes non-damaged objects. Comparing our network with YOLOv3, YOLOv4, cascade R-CNN, and other networks, the results show that our network can identify the damage of composite materials more accurately." @default.
• W4200419654 created "2021-12-31" @default.
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• W4200419654 date "2021-12-01" @default.
• W4200419654 modified "2023-10-09" @default.
• W4200419654 title "Intelligent damage recognition of composite materials based on deep learning and ultrasonic testing" @default.
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• W4200419654 doi "https://doi.org/10.1063/5.0063615" @default.
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