• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Computer-Aided Civil and Infrastructure Engineering >Damage Classification for Masonry Historic Structures Using Convolutional Neural Networks Based on Still Images
【24h】

Damage Classification for Masonry Historic Structures Using Convolutional Neural Networks Based on Still Images

机译：基于静止图像的卷积神经网络对砌体历史建筑物的损伤分类

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

Manual inspection (i.e., visual inspection and/or with professional equipment) is the most predominant approach for identifying and assessing superficial damage of masonry historic structures at present. However, this method is costly and at times difficult to apply to remote structures or components. Existing convolutional neural network (CNN)-based damage detection methods have not been specifically designed for the multiple damage identification of masonry historic structures. To overcome these limits, a deep architecture of CNN damage classification techniques for masonry historic structures is proposed in this article using a sliding window-based CNN method to identify and locate four categories of damage (intact, crack, efflorescence, and spall) with an accuracy of 94.3%. This is the first attempt to identify the multidamage of historic masonry structures based on CNN techniques and achieve excellent classification results. The data are only trained and tested from images of the Forbidden City Wall in China, and the pixel resolutions of stretcher brick images and header brick images are 480 x 105 and 210 x 105, respectively. Two CNNs (AlexNet and GoogLeNet) are both trained on a small dataset (2,000 images for training, 400 images for validation and testing) and a large dataset (20,000 images for training, 4,000 images for validation and testing). The performance of the trained model (94.3% accuracy) is examined on five new images with 1,860 x 1,260 pixel resolutions.
机译：手动检查（即目视检查和/或使用专业设备）是目前识别和评估砌体历史建筑物表面损坏的最主要方法。但是，该方法成本高昂，有时难以应用于远程结构或组件。现有的基于卷积神经网络（CNN）的损伤检测方法尚未专门设计用于砌体历史建筑物的多重损伤识别。为了克服这些限制，本文提出了一种用于砌体历史结构的CNN损伤分类技术的深层结构，该方法使用基于滑动窗口的CNN方法来识别和定位具有破坏性的四类损伤（完整，裂纹，起花和剥落）。准确性为94.3％。这是基于CNN技术识别历史性砖石结构多损伤并获得出色分类结果的首次尝试。仅根据中国紫禁城墙的图像对数据进行训练和测试，担架砖图像和标头砖图像的像素分辨率分别为480 x 105和210 x 105。两个CNN（AlexNet和GoogLeNet）都在一个小型数据集（用于训练的2,000张图像，用于验证和测试的400张图像）和大型数据集（用于训练的20,000张图像，用于验证和测试的4,000张图像）上进行训练。在5张分辨率为1,860 x 1,260像素的新图像上检查了训练模型的性能（准确度为94.3％）。

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