基于BF-Net与孪生分差的飞机结构裂纹检测方法  

Crack Detection of Aircraft Structures Based on BF-Net and Siamese Difference

作　　者：王叶子 吕帅帅[1] 杨宇[1] 王彬文[1] WANG Yezi;LYU Shuaishuai;YANG Yu;WANG Binwen(Aircraft Strength Research Institute of China,Xi′an,710065,China)

机构地区：[1]中国飞机强度研究所,西安710065

出　　处：《振动．测试与诊断》2023年第1期9-15,194,共8页Journal of Vibration,Measurement & Diagnosis

基　　金：中国飞机强度研究所创新基金资助项目(BYST-CKKJ-20-027);航空基金(青年基金)资助项目(2020Z061023001)。

摘　　要：针对目前飞机结构疲劳试验损伤检测中计算机视觉识别模型面临的主要问题是如何从同为毫米级的表面纹理、划痕、污迹中识别出疲劳裂纹,提出了基于双向融合网络(bidirectional fusion network,简称BF-Net)和孪生分差的飞机结构裂纹检测方法。首先,采用分级检测策略,提取出可能出现裂纹的重点区域;其次,设计面向微小目标识别的BF-Net,通过自上而下和自下而上的特征融合操作从区域图像中自动获取高质量的裂纹特征信息;最后,采用孪生分差法,基于重点区域的自身特征信息以及与模板间的特征差异信息来综合判别裂纹。试验结果表明,该方法能够实现对微小裂纹的精确检测,为飞机结构疲劳试验中裂纹的自动化检测提供了有效的技术途径。Crack detection is the main task of full-size aircraft structural fatigue test. Crack recognition based on computer vision can replace manual visual inspection to achieve timely, accurate and reliable damage detection in large, complex and dangerous test environment. At present, the main challenge of computer vision recognition model is how to identify fatigue cracks from surface textures, scratches and stains at the same millimeter scale. Therefore, a crack detection method for aircraft structures based on bidirectional fusion network(BF-Net) and siamese difference is proposed. Firstly, a hierarchical detection strategy is employed to extract the key regions where cracks may occur. Then, the BF-Net for small object recognition is designed to extract high-quality feature presentation of crack from regional images, through top-down and bottom-up feature fusion operations. Finally, the siamese difference is proposed, to comprehensively identify cracks, based on the characteristic information of the key region and the characteristic difference information between the region and the template. Experimental results illustrate that the method proposed in this paper can realize the accurate detection for small cracks, indicating a promising technique for automatic crack detection in fatigue test.

关 键 词：裂纹检测 计算机视觉 深度学习 双向融合网络 孪生分差 

分 类 号：TH878[机械工程—仪器科学与技术]