基于迁移学习的生成对抗网络的混凝土结构多类损伤检测

Kyle Dunphy, A. Sadhu, Jinfei Wang
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引用次数: 10

摘要

世界上现有的大量基础设施正在达到其使用寿命,需要以结构修复或更换的形式进行干预。这种资产管理的一个关键方面是对这些结构进行状况评估,以评估其现有健康状况,并规定所需修复的时间表和程度。已经证明,基于人工的人工检查面临后勤限制,并且昂贵、耗时长、主观,这取决于检查的知识。最近,基于自主视觉的技术已经被提出作为一种替代的,更准确的方法来检查恶化的结构。卷积神经网络(cnn)在混凝土结构损伤分类方面已经证明了最先进的准确性,并且经常用于处理来自相机、智能手机和无人机等基于视觉的传感器的图像。然而,这些原型需要一个大型的注释图像数据库来训练网络到一个准确的水平,这对于现实生活中的结构来说是不容易获得的。此外,cnn受到训练程度的限制;它们通常只训练用于单一材料模型的二元损伤分类。本文通过将生成对抗网络(gan)应用于混凝土结构的多类别损伤检测,解决了cnn的这些挑战。所提出的GAN使用SDNET2018数据集进行训练,以检测混凝土表面的开裂、剥落、点蚀和施工接缝。此外,还实现了迁移学习,将GAN的学习特征转移到CNN架构中,以实现准确的图像分类。结论是,对于使用的标记数据量减少0%-30%,所提出的GAN方法具有与传统cnn相当的精度。
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Multiclass damage detection in concrete structures using a transfer learning‐based generative adversarial networks
A large amount of the world's existing infrastructure is reaching the end of its service life, requiring intervention in the form of structural rehabilitation or replacement. A critical aspect of such asset management is the condition assessment of these structures to evaluate their existing health and dictate the scheduling and extent of required rehabilitation. It has been demonstrated that human‐based manual inspections face logistical constraints and are expensive, time extensive, and subjective, depending on the knowledge of the inspection. Recently, autonomous vision‐based techniques have been proposed as an alternative, more accurate method for the inspection of deteriorating structures. Convolutional neural networks (CNNs) have demonstrated state‐of‐the‐art accuracy with respect to damage classification for concrete structures and are often implemented to process images taken from vision‐based sensors such as cameras, smartphones, and drones. However, these archetypes require a large database of annotated images to train the network to an accurate level, which is not readily available for real‐life structures. Moreover, CNNs are limited to the extent by which they are trained; they are often only trained for binary damage classification of a singular material model. This paper addresses these challenges of CNNs through the application of a generative adversarial network (GANs) for multiclass damage detection of concrete structures. The proposed GAN is trained using the SDNET2018 dataset to detect cracking, spalling, pitting, and construction joints in concrete surfaces. Moreover, transfer learning is implemented to transfer the learned features of the GAN to a CNN architecture to allow for accurate image classification. It is concluded that, for a 0%–30% reduction in the amount of labeled data used, the proposed GAN method has comparable accuracy to traditional CNNs.
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