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首页> 外文期刊>IEEE transactions on industrial informatics >HDCB-Net: A Neural Network With the Hybrid Dilated Convolution for Pixel-Level Crack Detection on Concrete Bridges
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HDCB-Net: A Neural Network With the Hybrid Dilated Convolution for Pixel-Level Crack Detection on Concrete Bridges

机译：HDCB-NET：一种神经网络，具有混凝土桥梁对像素级裂纹检测的混合扩张卷积

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摘要

Crack detection on concrete bridges is a critical task to ensure bridge safety. However, many cracks on concrete bridges show low contrast and blurry edges in practice, which brings challenges to image-based crack detection. In this article, to improve the detection accuracy of blurred cracks, we propose the HDCB-Net-a deep learning-based network with the hybrid dilated convolutional block (HDCB) for the pixel-level crack detection. Specifically, HDCB is employed to expand the receptive field of the convolution kernel without increasing the computational complexity and to avoid the gridding effect generated by the dilated convolution. Meanwhile, to achieve a reasonable efficiency/accuracy tradeoff, the HDCB-Net only contains a few downsampling stages, which can avoid the loss of blurred crack pixels due to excessive downsampling. Furthermore, a two-stage strategy is proposed to realize the fast crack detection in a massive number of images (more than 100 000) with the high resolution (5120 x 5120 pixels). At the first stage, YOLOv4 is employed to filter out images without cracks and generate coarse region proposals. At the second stage, to achieve refined damage analysis, the HDCB-Net is used to detect pixel-level cracks from the coarse region proposals. The experimental results demonstrate that the proposed HDCB-Net is genetic and able to improve the detection accuracy of blurred cracks, and our two-stage strategy is efficient for fast crack detection. The whole detection process takes only 0.64 s to handle a single image. Additionally, we have established a public dataset, including 150 632 high-resolution images, dedicated to the research of crack detection, which have been released along with this article.
机译：混凝土桥梁的裂缝检测是保证桥梁安全的重要任务。然而，在混凝土桥梁多处裂缝显示对比度低，边缘模糊在实践中，这给基于图像的裂纹检测的挑战。在这篇文章中，为了提高模糊裂纹的检测精度，我们提出HDCB净深基于学习的网络与混合扩张的像素级裂缝检测卷积块（HDCB）。具体而言，HDCB采用扩大卷积核的感受域，而不增加计算复杂度，并避免由扩张的卷积产生的网格化效果。同时，为了达到合理的效率/精度的折衷，该HDCB-Net的只包含几个阶段下采样，其可以避免模糊裂纹像素的损失，由于过度的下采样。此外，两阶段策略提出了实现与高分辨率（5120 X 5120像素）的图像（100多000）的大规模数量的快速裂纹检测。在第一阶段，YOLOv4被用来滤除图像无裂纹和产生粗区域的建议。在第二阶段，以达到成品损伤分析中，HDCB-Net的用于从粗区域的建议检测像素级的裂纹。实验结果表明，该HDCB-Net是遗传和能够提高模糊裂缝的检测精度，而我们的两阶段的策略是有效的快速裂纹检测。整个检测过程仅需0.64秒至处理单个图像。此外，我们已经建立了一个公共数据集，包括150 632的高分辨率图像，致力于裂纹检测，已被释放随着这篇文章的研究。

著录项

  • 来源
    《IEEE transactions on industrial informatics》 |2021年第8期|5485-5494|共10页
  • 作者

    Jiang Wenbo; Liu Min; Peng Yunuo; Wu Lehui; Wang Yaonan;

  • 作者单位

    Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China|Hunan Univ Natl Engn Lab Robot Visual Percept & Control Tech Changsha 410082 Hunan Peoples R China;

    Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China|Hunan Univ Natl Engn Lab Robot Visual Percept & Control Tech Changsha 410082 Hunan Peoples R China;

    Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China|Hunan Univ Natl Engn Lab Robot Visual Percept & Control Tech Changsha 410082 Hunan Peoples R China;

    Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China|Hunan Univ Natl Engn Lab Robot Visual Percept & Control Tech Changsha 410082 Hunan Peoples R China;

    Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China|Hunan Univ Natl Engn Lab Robot Visual Percept & Control Tech Changsha 410082 Hunan Peoples R China;

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  • 正文语种 eng
  • 中图分类
  • 关键词

    Bridges; Robots; Convolution; Proposals; Image resolution; Inspection; Kernel; Deep learning; fast crack detection; HDCB-Net; hybrid dilated convolution; two-stage strategy;

    机译：桥梁;机器人;卷积;提案;图像分辨率;检查;内核;深入学习;快速裂纹检测;HDCB-NET;杂交扩张卷积;两级战略;

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