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《工程(英文)》 >> 2021年 第7卷 第12期 doi: 10.1016/j.eng.2020.07.026

基于频域深度学习的混凝土桥面裂缝实时检测

a Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA
b Wiss, Janney, Elstner Associates (WJE) Inc., Northbrook, IL 60062, USA
c Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan 41354, China

收稿日期: 2020-04-18 修回日期: 2020-06-09 录用日期: 2020-07-27 发布日期: 2020-11-19

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摘要

本文提出了一种基于视觉的混凝土桥面裂缝检测方法,该检测方法将一维卷积神经网络(1D-CNN)与长短期记忆(LSTM)方法集成在图像频域。1D-CNN-LSTM算法经过了数千张开裂以及非开裂的混凝土桥面图像的训练。为了提高训练效率,在预处理阶段,图像首先会被转换到频域,然后使用扁平化的频率数据对算法进行校准。LSTM则被用来提高针对长序列数据开发的网络的性能。所开发模型的训练、验证与测试数据的准确率分别为99.05%、98.9%和99.25%。随后,本文进一步提出了一个运行框架,以便在未来将经过训练的模型应用于大尺寸图像。与现有的深度学习方法相比,本文所提出的1D-CNN-LSTM算法在准确度与计算时间等方面均有卓越的表现。可以预见,1D-CNN-LSTM算法的快速运行使其在实时裂缝检测领域必将大有可为。

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