基于改进FaceNet的飞行器结构裂纹识别方法

doi:10.7527/S1000-6893.2021.25463

Abstract

Abstract: The automatic crack identification algorithm based on computer vision has a good engineering application prospect in aircraft full-scale fatigue test. However, due to the diversity of aircraft structures and the complexity of fatigue test environment, the direct application of the existing target detection algorithm will have a high misjudgment rate. Therefore, this paper proposes a crack identification method based on state comparison of key structure. Based on the face recognition model FaceNet, contrast mechanism is used to eliminate interference of structure surface texture and scratches, and through the analysis of crack data structure and characteristic distribution law, sample generation rules, network architecture and the loss function of FaceNet are improved adaptively. The model is sensitive to cracks and has low demand on image quality. In the test environment, the detection accuracy of the proposed method is 97.6% for the crack length of 0.2-5 mm, which has obvious advantages over the existing methods.

Key words: fatigue test, computer vision, crack, deep learning, FaceNet, target detection

CLC Number:

V219

LYU Shuaishuai, YANG Yu, WANG Binwen, YIN Chenfei. A crack identification method of aircraft structure based on improved FaceNet[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(6): 525463-525463.

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A crack identification method of aircraft structure based on improved FaceNet

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