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Recovering compressed images for automatic crack segmentation using generative models

机译：使用生成模型恢复用于自动裂缝分割的压缩图像

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摘要

In a structural health monitoring (SHM) system that uses digital cameras to monitor cracks of structural surfaces, techniques for reliable and effective data compression are essential to ensure a stable and energy-efficient crack images transmission in wireless devices, e.g., drones and robots with high definition cameras installed. Compressive sensing (CS) is a signal processing technique that allows accurate recovery of a signal from a sampling rate much smaller than the limitation of the Nyquist sampling theorem. Different from the popular approach of simultaneously training encoder and decoder using neural network models, the CS theory ensures a high probability of accurate signal reconstruction based on random measurements that is shorter than the length of the original signal under a sparsity constraint. Such method is particularly useful when measurements are expensive, such as wireless sensing of civil structures, because its hardware implementation allows down sampling of signals during the sensing process. Hence, CS methods can achieve significant energy saving for the sensing devices. However, the strong assumption of the signals being highly sparse in an invertible space is relatively hard to guarantee for many real images, such as image of cracks. In this paper, we present a new approach of CS that replaces the sparsity regularization with a generative model that is able to effectively capture a low dimension representation of targeted images. We develop a recovery framework for automatic crack segmentation of compressed crack images based on this new CS method. We demonstrate the remarkable performance of our method that takes advantage of the strong capability of generative models to capture the necessary features required in the crack segmentation task even the backgrounds of the generated images are not well reconstructed. The superior performance of our recovery framework is illustrated by comparisons to three existing CS algorithms. Furthermore, we show that our framework is potentially extensible to other common problems in automatic crack segmentation, such as defect recovery from motion blurring and occlusion.
机译：在使用数码相机的结构健康监测（SHM）系统中监测结构表面的裂缝，可靠且有效的数据压缩技术对于确保无线设备中的稳定和节能裂缝图像传输，例如无人驾驶和机器人是必不可少的安装了高清摄像头。压缩检测（CS）是一种信号处理技术，其允许从采样率的准确恢复远小于奈奎斯特采样定理的限制。与使用神经网络模型的同时训练编码器和解码器的流行方法不同，CS理论可确保基于基于稀疏性约束下的原始信号长度短的随机测量的精确信号重建的高概率。当测量昂贵时，这种方法是特别有用的，例如公用结构的无线感测，因为其硬件实现允许在感测过程期间向上采样信号。因此，CS方法可以实现传感设备的显着节能。然而，在可逆空间中对信号高度稀疏的强烈假设相对难以保证许多真实图像，例如裂缝的图像。在本文中，我们呈现了一种新方法，其CS用一种能够有效地捕获目标图像的低维表示的生成模型来替换稀疏正则化。基于此新CS方法，我们为压缩裂纹图像的自动裂纹分割开发恢复框架。我们展示了我们的方法的显着性能，它利用生成模型的强大能力来捕获裂缝分割任务所需的必要特征，即使产生的图像的背景也不是很好的重建。通过比较为三种现有的CS算法来说明我们恢复框架的卓越性能。此外，我们表明我们的框架可能与自动裂纹分割中的其他常见问题潜在地扩张，例如运动模糊和遮挡的缺陷恢复。

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  • 来源
    《Mechanical systems and signal processing》 |2021年第1期|107061.1-107061.17|共17页
  • 作者

    Yong Huang; Haoyu Zhang; Hui Li; Stephen Wu;

  • 作者单位

    Key Laboratory of Intelligent Disaster Prevention and Mitigation for Civil Infrastructures Ministry of Industry and Information Technology Harbin Heilongjiang 150090 China Ministry of Education Key Laboratory of Structural Dynamic Behavior and Control School of Civil Engineering Harbin Institute of Technology Harbin China;

    Key Laboratory of Intelligent Disaster Prevention and Mitigation for Civil Infrastructures Ministry of Industry and Information Technology Harbin Heilongjiang 150090 China Ministry of Education Key Laboratory of Structural Dynamic Behavior and Control School of Civil Engineering Harbin Institute of Technology Harbin China;

    Key Laboratory of Intelligent Disaster Prevention and Mitigation for Civil Infrastructures Ministry of Industry and Information Technology Harbin Heilongjiang 150090 China Ministry of Education Key Laboratory of Structural Dynamic Behavior and Control School of Civil Engineering Harbin Institute of Technology Harbin China;

    The Institute of Statistical Mathematics Research Organization of Information and Systems 10-3 Midori-cho Tachikawa Tokyo 190-8562 Japan The Graduate University for Advanced Studies SOKENDAI 10-3 Midori-cho Tachikawa Tokyo 190-8562 Japan;

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  • 正文语种 eng
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  • 关键词

    Generative model; Compressive sensing; Generative adversarial network; Crack segmentation;

    机译：生成模型;压缩感应;生成对抗性网络;裂缝分割;

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