基于全卷积神经网络的桥梁裂缝分割和测量方法

胡文魁; 邓晖; 付志旭; 安栋阳; 段锐

doi:10.13204/j.gyjzG21053111

基于全卷积神经网络的桥梁裂缝分割和测量方法

doi: 10.13204/j.gyjzG21053111

华南理工大学土木与交通学院, 广州 510641

基金项目:

自然资源部城市国土资源监测与仿真重点实验室开放课题基金(KF-2019-04-024)。

详细信息

作者简介:
胡文魁,男,1997年出生,硕士。

通讯作者:
付志旭,1565930489@qq.com。

计量
- 文章访问数: 175
- HTML全文浏览量: 38
- PDF下载量: 6
- 被引次数: 0
出版历程
- 收稿日期: 2021-05-31
- 网络出版日期: 2022-07-25

Bridge Crack Segmentation and Measurement Method Based on Full Convolutional Neural Network

School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China

摘要

摘要: 为了提高桥梁病害检测自动化水平,解决当前人工检测耗时费力和传统图像分割方法存在去噪效果不明显、分割后裂缝连续性较差等问题,提出了一种基于全卷积神经网络的BCI-AS (Bridge Crack Image-Automatic Segmentation)桥梁裂缝自动分割模型和一种基于投影技术的最小二乘拟合中心线的裂缝宽度测量算法。基于BCI-AS的模型对桥梁裂缝图像数据集进行了精确的像素级分割,分割准确率达到94.45%。基于投影技术最小二乘拟合中心线的算法对分割的裂缝二值图进行了宽度测量,结果表明相对误差在7%以下,证明了所提出具体算法对裂缝分割和裂缝宽度计算的可行性。
- 桥梁裂缝 /
- 图像分割 /
- 全卷积神经网络 /
- 最小二乘拟合
Abstract: In order to improve the automation level of bridge disease detection, solve the current manual detection time-consuming and laborious, traditional image segmentation methods have problems such as non-obvious denoising effect and poor crack continuity after segmentation. A bridge crack automatic segmentation model of BCI-AS(Bridge Crack Image-Automatic Segmentation) based on full convolutional neural network and a crack width measurement algorithm of least square fitting center line based on projection technology were proposed.Based on the BCI-AS model, the image data set of bridge cracks was segmented at the pixel level accurately, and the segmentation accuracy reached 94.45%.The width of the segmented crack binary map was measured by the least square center line fitting algorithm based on the projection technology. The results showed that the relative error was less than 7%, which could prove the feasibility of the proposed algorithm for crack segmentation and crack width calculation.
- bridge cracks /
- image segmentation /
- full convolutional neural network /
- least squares fitting

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参考文献(21)

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