• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Composite Structures >A deep transfer learning model for inclusion defect detection of aeronautics composite materials
【24h】

A deep transfer learning model for inclusion defect detection of aeronautics composite materials

机译：空间复合材料缺陷检测深度转移学习模型

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Composite materials are increasingly used as structural components in military and civilian aircraft. To ensure their high reliability, numerous non-destructive testing (NDT) techniques have been used to detect defects during production and maintenance. However, most of these techniques are non-automatic, with diagnostic results determined subjectively by operators. Some deep learning methods have been proposed to identify defects in images obtained through NDT, but they need labeled image samples with defects, which can be expensive or unavailable. We propose a deep transfer learning model to accurately extract features for the inclusion of defects in X-ray images of aeronautics composite materials (ACM), whose samples are scarce. We researched an automatic inclusion defect detection method for X-ray images of ACM using our proposed model. Experimental results show that the model can reach 96% classification accuracy (F1 measure) with satisfactory detection results.
机译：复合材料越来越多地用作军用和民用飞机的结构部件。为确保其高可靠性，众多的非破坏性测试（NDT）技术已被用于检测生产和维护期间的缺陷。然而，这些技术中的大多数是非自动的，具有操作员主体确定的诊断结果。已经提出了一些深入的学习方法来识别通过NDT获得的图像中的缺陷，但它们需要具有缺陷的标记图像样本，这可以是昂贵或不可用的。我们提出了一种深度转移学习模型，以准确提取用于在航空复合材料（ACM）的X射线图像中包含缺陷的特征，其样品稀缺。我们使用我们提出的模型研究了ACM的X射线图像的自动包裹缺陷检测方法。实验结果表明，该模型可达到96％的分类精度（F1测量），检测结果令人满意。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号