基于改进FaceNet的飞行器结构裂纹识别方法

doi:10.7527/S1000-6893.2021.25463

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基于改进FaceNet的飞行器结构裂纹识别方法

吕帅帅, 杨宇, 王彬文, 殷晨飞

中国飞机强度研究所, 西安 710065

收稿日期:2021-03-09 修回日期:2021-09-07 出版日期:2022-06-15 发布日期:2021-09-06
通讯作者: 杨宇,E-mail:yangyu@cae.ac.cn E-mail:yangyu@cae.ac.cn
基金资助:
中国飞机强度研究所创新基金（BYST-CKKJ-20-027）；航空科学基金（2020Z061023001）

A crack identification method of aircraft structure based on improved FaceNet

LYU Shuaishuai, YANG Yu, WANG Binwen, YIN Chenfei

Aircraft Strength Research Institute of China, Xi'an 710065, China

Received:2021-03-09 Revised:2021-09-07 Online:2022-06-15 Published:2021-09-06
Supported by:
Innovation Fund of China Aircraft Strength Research Institute (BYST-CKKJ-20027);Aeronautical Science Foundation of China (2020Z061023001)

摘要/Abstract

摘要： 基于计算机视觉的裂纹自动识别算法在飞机全尺寸疲劳试验中具有较好的工程应用前景。但由于飞机结构构型多样、疲劳试验环境复杂，直接应用现有的目标检测算法会存在较高的误判率。因此，提出一种基于关键部位状态对比的裂纹识别方法，以人脸识别模型FaceNet为基础，利用对比机制消除结构表面纹理、划痕等干扰因素的影响，并通过对裂纹数据结构和特征分布规律的分析，对FaceNet模型的样本生成规则、网络架构和损失函数进行了适应性改进。该方法具有对裂纹敏感、对图像质量要求低的特点。在疲劳试验环境中，该方法对长度为0.2~5 mm裂纹的检测准确率为97.6%，相较于现有方法优势明显。

关键词: 疲劳试验, 计算机视觉, 裂纹, 深度学习, FaceNet, 目标检测

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

中图分类号:

V219

吕帅帅, 杨宇, 王彬文, 殷晨飞. 基于改进FaceNet的飞行器结构裂纹识别方法[J]. 航空学报, 2022, 43(6): 525463-525463.

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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E-mail：hkxb@buaa.edu.cn

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主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

基于改进FaceNet的飞行器结构裂纹识别方法

A crack identification method of aircraft structure based on improved FaceNet

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