Robust multitask compressive sampling via deep generative models for crack detection in structural health monitoring

IF 5.7 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Haoyu Zhang, Stephen Wu, Yong Huang, Hui Li
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引用次数: 0

Abstract

In structural health monitoring (SHM), there is an increasing demand for real-time image-based damage detection. Such a technology is essential for minimizing hazard loss caused by delayed emergency response after earthquakes or other natural disasters, or service interruption during structural inspection. Compressive sampling (CS) is a promising solution to achieve such a goal by greatly reducing the power consumption on high-resolution image transmission when using wireless devices. However, conventional CS failed to achieve high enough compression ratios, while existing generative-model-based CS requires laboriously training a high-quality generator with many large-scale images. To overcome such a bottleneck that hinders the practical use of CS in SHM, we propose a multitask CS algorithm that only relies on existing generators trained by low-pixel crack images. By exploiting the new discovery that similar crack images share a similar sparsity pattern in their latent vectors mapped by the generator, our algorithm achieves higher crack detection accuracy and robustness within a much shorter time when using a high data compression ratio. We verify the effectiveness of the proposed CS algorithm using synthetic and real image data. The results demonstrate that this work has moved a step closer toward successful implementation of operational CS-based crack detection systems in real-time SHM.
基于深度生成模型的鲁棒多任务压缩采样用于结构健康监测中的裂纹检测
在结构健康监测(SHM)中,基于图像的实时损伤检测的需求越来越大。这种技术对于最大限度地减少地震或其他自然灾害后应急响应延迟或结构检查期间服务中断所造成的危害损失至关重要。压缩采样(CS)是实现这一目标的一个很有前途的解决方案,它在使用无线设备时大大降低了高分辨率图像传输的功耗。然而,传统的CS无法获得足够高的压缩比,而现有的基于生成模型的CS需要费力地训练具有许多大规模图像的高质量生成器。为了克服这种阻碍CS在SHM中实际应用的瓶颈,我们提出了一种多任务CS算法,该算法仅依赖于由低像素裂纹图像训练的现有生成器。通过利用相似的裂纹图像在其生成器映射的潜在向量中具有相似的稀疏模式的新发现,我们的算法在使用高数据压缩比的情况下在更短的时间内实现了更高的裂纹检测精度和鲁棒性。我们使用合成和真实图像数据验证了所提出的CS算法的有效性。结果表明,这项工作已经朝着在实时SHM中成功实施可操作的基于cs的裂缝检测系统迈进了一步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
12.80
自引率
12.10%
发文量
181
审稿时长
4.8 months
期刊介绍: Structural Health Monitoring is an international peer reviewed journal that publishes the highest quality original research that contain theoretical, analytical, and experimental investigations that advance the body of knowledge and its application in the discipline of structural health monitoring.
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