Recognition of the internal situation of aircraft skin based on deep learning

Abstract

The aircraft skin is an important component of the aircraft, and its integrity affects the flight performance and safety performance of the aircraft. Damage detection technology with ultrasonic nondestructive testing as the core has played an important role in aircraft skin damage detection. Due to the difficulty in aircraft skin detection, relying solely on the ultrasonic A-scan equipment has very low detection efficiency. The introduction of artificial intelligence can effectively improve the detection efficiency. This paper establishes the one-dimensional convolutional neural network and single shot multibox detector network, which is based on the SSD network and uses dilated convolution to improve the real-time tracking of the ultrasonic probe. At the same time, 1DCNN is introduced to classify the ultrasonic A-scan signal. Finally, the detection results of the two are combined to achieve the reflection of the internal conditions of the aircraft skin. After testing, the algorithm can identify skin simulation specimens, and its recognition accuracy is 96.5%, AP is 90.9%, and kappa is 0.996. Comparing the improved SSD network with networks such as SSD, YOLOv3, and Faster R-CNN, the results show that the improved network used in this paper is more excellent and effective. At the same time, the detection effects of four types of optimization algorithms and five learning rates are studied, and finally, the corresponding signal classification model adopts Adam and the learning rate of 0.0001 has the best effect.