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Hybrid of TSR and Seeded Region Growing for Debonding Detection Using Optical Thermography

机译:用于使用光学热成像的剥离检测的TSR和种子区域的杂种

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Carbon fiber reinforced polymer (CFRP) are commonly used in the field of aerospace. Under manufacturing or in service procedure, there exists internal defects such as delamination and debonding due to the factors of improper production and environment. In order to guarantee CFRP internal quality and safety, the optical pulsed thermography (OPT) nondestructive testing has been used to detect the internal defects. However, the current OPT related methods has problems of uneven illumination and low resolution of defects detection. In this paper, the hybrid of thermographic signal reconstruction (TSR) and seeded region growing (SRG) algorithm was proposed to deal with the infrared thermal image sequences of the CFRP specimen, which can significantly enhance the detection rate. Finally, the event based F-score is computed to measure the detection results and comparison studies show that the proposed method can improve the performance of the detection.
机译:碳纤维增强聚合物(CFRP)通常用于航空航天领域。在制造业或服务程序下,由于生产和环境不当的因素,存在内部缺陷,如分层和剥离。为了保证CFRP内部质量和安全性,光学脉冲热成像(OPT)无损检测已被用于检测内部缺陷。然而,当前的选择相关方法具有不均匀的照明和低分辨率检测的问题。在本文中,提出了热成像重建(TSR)和种子区域生长(SRG)算法的混合,以处理CFRP样本的红外热图像序列,这可以显着提高检测率。最后,计算基于事件的F分数以测量检测结果和比较研究表明,该方法可以提高检测的性能。

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