CN114689600A - 一种桥梁混凝土结构表面裂缝检测方法及系统 - Google Patents

一种桥梁混凝土结构表面裂缝检测方法及系统 Download PDF

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CN114689600A
CN114689600A CN202210336041.2A CN202210336041A CN114689600A CN 114689600 A CN114689600 A CN 114689600A CN 202210336041 A CN202210336041 A CN 202210336041A CN 114689600 A CN114689600 A CN 114689600A
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邹星星
张锋
王立彬
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Abstract

本发明公开了一种桥梁混凝土结构表面裂缝检测方法及系统,属于混凝土结构表面损伤检测技术领域,包括:远程操作工业无人机对桥梁混凝土结构表面进行拍摄,获取混凝土表面图片;对获取混凝土表面照片进行图片拼接,获取桥梁混凝土结构表面全景图像;将桥梁混凝土结构表面全景图像输入卷积神经网络(Convolutional Neural Networks,CNN)模型,将识别存在表面裂缝的区域进行标记,最后将标记完成的结果输出到全景图像中。相比传统的桥梁健康监测方法,能够以图片输入、以图片输出,快速准确识别目标,无需大量人力物力,具有实际工程意义。

Description

一种桥梁混凝土结构表面裂缝检测方法及系统
技术领域
本发明涉及桥梁混凝土结构损伤检测技术领域,特别是涉及一种桥梁混凝土结构表面裂缝检测方法及系统。
背景技术
我国在基础建设取得卓越成效的过程中建设了大规模的混凝土桥梁,但现如今的桥梁建设发展已经进入了维护检修周期,桥梁经过了数十年的工作服役,不可避免地会在桥梁混凝土结构表面产生大量裂缝。结构关键部位如果出现裂缝,将显著降低桥梁承载力,使桥梁暂停运行数周乃至数月,这将大大降低城市运输效率,影响人们出行,严重情况下甚至可能造成桥梁崩塌,出现大规模人员伤亡事故,对桥梁上行人与过往车辆的行驶安全造成严重威胁。因此,裂缝的产生与发展对桥梁的服役健康有着至关重要的影响,需要建立一套成熟可靠且经济便捷的桥梁损伤检测系统,对桥梁进行长期定期的检测,保证混凝土桥梁服役工作的安全。
目前桥梁检测的主要方法还停留在使用桥梁检测车,桥梁检测车分为折叠臂式和桁架式两种,将检测人员托送至桥梁底部或侧面进行人工目视检测,但是这种方法不仅需要耗费大量人力物力,效率十分低下,桥梁检测车的检测平台伸缩经常收到桥侧电线杆与护栏的影响,而且人工检测容易出现安全事故,检测人员的作业风险较高。
为解决上述问题,本发明提出一种桥梁混凝土结构表面裂缝检测方法及系统。该检测方法及系统通过驾驶工业无人机对桥梁混凝土结构表面进行拍摄,获取裂缝图片,对获取裂缝图片进行图片拼接,获取桥梁混凝土结构表面全景图像,将桥梁混凝土结构表面全景图像输入卷积神经网络模型,将识别存在裂缝的区域进行标记,最后将标记完成的结果输出到全景图像中,如同设计图纸一般的桥梁整体全景图像,可向工程师提供直观的检测结果。相比传统的桥梁损伤检测方法,能够以图片输入、以图片输出,快速准确识别目标,无需大量人力物力,降低人员作业风险,有效提升检验效率,可实现桥梁损伤检测系统的自动化流程,显著降低桥梁运维成本,具有实际工程意义。
发明内容
针对现有技术存在的问题,本发明提供一种桥梁混凝土结构表面裂缝检测方法及系统,在提高检测效率的基础上,可实现桥梁损伤检测系统的自动化流程,并同时具备经济性和系统性,具有实际工程意义。
本发明的目的是通过以下技术方案来实现的:一种桥梁混凝土结构表面裂缝检测方法及系统,具体包括如下步骤:
(1)数据采集端:驾驶工业无人机对待测桥梁混凝土结构表面多区域图像进行拍摄;所述待测桥梁混凝土结构表面多区域图像之间相邻区域重叠率大于75%;将所述待测桥梁混凝土结构表面多区域图像传输至电脑。
(2)数据处理端:将待测桥梁混凝土结构表面多区域图像储存路径导入至图像拼接算法中,基于Harris角点检测相邻图像的关键点特征,基于SIFT算法对所述关键点特征进行不变特征描述符计算,基于所述关键点特征和不变特征描述符,在相邻图像上匹配点对,得到匹配点对,基于RANSAC算法和匹配的特征来估计单应矩阵,基于所述单应矩阵进行仿射变换,对待测桥梁混凝土结构表面多区域图像相邻图片进行拼接,获取桥梁混凝土结构表面全景图像;将所述桥梁混凝土结构表面全景图像输入卷积神经网络(ConvolutionalNeural Networks,CNN)模型,以所述桥梁混凝土结构表面全景图像的左下图像角点作为坐标原点建立二维坐标系,将全景图像分割成多个128*128像素的局部图片,分别对所述128*128像素的局部图片进行裂缝识别,所述卷积神经网络共有5个Block,前三个Block由3个卷积层组成,卷积层有32个滤波器,每个卷积层后面都连接Max Pooling和Dropout层,每个卷积层的激活函数均为ReLU,Block 4为一个GlobalAveragePooling2D层连接在卷积层后,Block 5是一个全连接层,最后是输出层,输出层激活函数使用Sigmoid函数,损失函数是二元交叉熵,使用Adam作为优化器,输出结果为“Negative”和“Positive”,识别出裂缝的图片为“Positive”,将识别为“Positive”的图片进行标记;将识别为“Positive”的图片及其坐标信息标记到桥梁混凝土结构表面全景图像上。
(3)结果输出端:最后将标记完成的桥梁混凝土结构表面全景图像输出。
有益效果:本发明采用卷积神经网络模型检测裂缝,3个卷积层可以在保证检测结果的准确性的同时提高检测速度,Max Pooling层可以减少计算参数的数量,降低运算成本,卷积层后连接的Dropout层可以有效地防止过拟合,该神经网络结构简洁有效,输出结果是如同设计图纸一般的桥梁整体全景图像,可向工程师提供直观的检测结果。相比传统的桥梁损伤检测方法,能够以图片输入、以图片输出,快速准确识别目标,无需大量人力物力,在提高检测效率的基础上,可实现桥梁损伤检测系统的自动化流程,有效降低桥梁运维成本,同时具备经济型和系统性,具有实际工程意义。
附图说明:
图1为本发明提供的一种桥梁混凝土结构表面裂缝检测方法及系统流程图;
图2为本发明提供的卷积神经网络模型结构示意图;
图3为本发明实施例实验中在裂缝图像识别训练时的损失曲线;
图4为本发明实施例实验中在裂缝图像识别训练时的准确率曲线;
图5为本发明实施例实验中卷积神经网络识别128*128像素图片的输出结果示例图。
具体实施方式:
为了使本发明的目的、技术方案及优点更加清楚明白,以下结合附图,对本发明进行进一步详细说明。
如图1为本发明提供的一种桥梁混凝土结构表面裂缝检测方法及系统流程图,具体包括如下步骤:
(1)数据采集端:驾驶工业无人机对待测桥梁混凝土结构表面多区域图像进行拍摄;所述待测桥梁混凝土结构表面多区域图像之间相邻区域重叠率应至少大于75%;拍摄顺序从桥梁混凝土结构的一端至另一端,保持同等高度,无人机拍摄倾角保持不变,无人机航向沿桥梁平行前进;将所述待测桥梁混凝土结构表面多区域图像传输至电脑。
(2)数据处理端:将待测桥梁混凝土结构表面多区域图像导入至图像拼接算法中,基于Harris角点检测相邻图像的关键点特征,基于SIFT算法对所述关键点特征进行不变特征描述符计算,基于所述关键点特征和不变特征描述符,在相邻图像上匹配点对,得到匹配点对,基于RANSAC算法和匹配的特征来估计单应矩阵,基于所述单应矩阵进行仿射变换,对待测桥梁混凝土结构表面多区域图像相邻图像进行拼接。在拼接图片的四边都添加5像素的边框,对拼接图片建立灰度背景并进行阈值处理,以白色作为拼接图像轮廓,以黑色作为背景,对轮廓进行提取,计算出拼接图像轮廓的边界框,提取感兴趣区域(Region ofInterest,ROI),最终获取桥梁混凝土结构表面全景图像。将所述桥梁混凝土结构表面全景图像输入卷积神经网络(Convolutional Neural Networks,CNN)模型,以所述桥梁混凝土结构表面全景图像的左下图像角点作为坐标原点建立二维坐标系,将全景图像分割成多个128*128像素的局部图片,分别对所述128*128像素的局部图片进行裂缝识别。图2为本发明提供的卷积神经网络模型结构示意图,所述卷积神经网络模型共有5个Block,前三个Block由3个卷积层组成,卷积层有32个滤波器,每个卷积层后面都连接Max Pooling和,MaxPooling层减少输入的参数,缩短运行时间,减少计算量,Dropout层增强网络的鲁棒性,使得网络不容易产生过拟合,可以保持较高的预测精度,每个卷积层的激活函数均为ReLU;Block 4为一个GlobalAveragePooling2D层连接在卷积层后;Block 5是一个全连接层,最后是输出层,输出层激活函数使用Sigmoid函数,损失函数是二元交叉熵,使用Adam作为优化器。图3为本发明实施例实验中在裂缝图像识别训练时的损失曲线,在50个训练周期(Epoch)中,损失值(Loss)从最初的0.16迅速下降到了0.0066,并趋于稳定,快速收敛。图4为本发明实施例实验中在裂缝图像识别训练时的准确率曲线,在50个训练周期(Epoch)中,准确率(Accuracy)从最初的0.9327上升到了0.9981,后续准确率保持在99%以上,该神经网络训练后用于裂缝识别的收敛速度良好,具有很好的鲁棒性和泛化能力。图五为本发明实施例实验中卷积神经网络识别128*128像素图片的输出图片示例图。将导入的多个128*128像素的局部图片分类为“Negative”和“Positive”,识别出裂缝的图片为“Positive”,未识别出裂缝的图片为“Negative”,将识别为“Positive”的图片进行标记,然后将识别为“Positive”的图片及其坐标信息标记到桥梁混凝土结构表面全景图像上。
(3)结果输出端:最后将标记完成的桥梁混凝土结构表面全景图像输出。完整的桥梁底面或侧面全景图像,令工程师更加直观进行裂缝损伤的识别和分类。
以上所述仅为本发明的较佳实施例而已,并不用以限制本发明,凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等,均应包含在本发明的保护范围之内。

Claims (1)

1.一种桥梁混凝土结构表面裂缝检测方法及系统,其特征在于,具体包括以下步骤:
(1)数据采集端:驾驶工业无人机对待测桥梁混凝土结构表面多区域图像进行拍摄;所述待测桥梁混凝土结构表面多区域图像之间相邻区域重叠率大于75%;将所述待测桥梁混凝土结构表面多区域图像传输至电脑。
(2)数据处理端:将待测桥梁混凝土结构表面多区域图像导入至图像拼接算法中,基于Harris角点检测相邻图像的关键点特征,基于SIFT算法对所述关键点特征进行不变特征描述符计算,基于所述关键点特征和不变特征描述符,在相邻图像上匹配点对,得到匹配点对,基于RANSAC算法和匹配的特征来估计单应矩阵,基于所述单应矩阵进行仿射变换,对待测桥梁混凝土结构表面多区域图像相邻图片进行拼接,获取桥梁混凝土结构表面全景图像;将所述桥梁混凝土结构表面全景图像输入卷积神经网络(Convolutional NeuralNetworks,CNN)模型,以所述桥梁混凝土结构表面全景图像的左下图像角点作为坐标原点建立二维坐标系,将全景图像分割成多个128*128像素的局部图片,分别对所述128*128像素的局部图片进行裂缝识别,所述卷积神经网络共有5个Block,前三个Block由3个卷积层组成,卷积层有32个滤波器,每个卷积层后面都连接Max Pooling和Dropout层,每个卷积层的激活函数均为ReLU,Block 4为一个GlobalAveragePooling2D层连接在卷积层后,Block5是一个全连接层,最后是输出层,输出层激活函数使用Sigmoid函数,损失函数是二元交叉熵,使用Adam作为优化器,输出结果为“Negative”和“Positive”,识别出裂缝的图片为“Positive”,将识别为“Positive”的图片进行标记;将识别为“Positive”的图片及其坐标信息标记到桥梁混凝土结构表面全景图像上。
(3)结果输出端:最后将标记完成的桥梁混凝土结构表面全景图像输出。
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CN115311254A (zh) * 2022-09-13 2022-11-08 万岩铁路装备(成都)有限责任公司 基于Harris-SIFT算法的钢轨轮廓匹配方法
CN115790400A (zh) * 2023-01-17 2023-03-14 中大智能科技股份有限公司 一种应用于桥隧结构安全的机器视觉标靶标定方法
CN115953672A (zh) * 2023-03-13 2023-04-11 南昌工程学院 一种水下大坝表面裂缝识别方法
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CN115311254A (zh) * 2022-09-13 2022-11-08 万岩铁路装备(成都)有限责任公司 基于Harris-SIFT算法的钢轨轮廓匹配方法
WO2024080436A1 (ko) * 2022-10-11 2024-04-18 주식회사 에프디 교각 구동장치를 이용한 ai 균열 검출방법
CN115790400A (zh) * 2023-01-17 2023-03-14 中大智能科技股份有限公司 一种应用于桥隧结构安全的机器视觉标靶标定方法
CN115953672A (zh) * 2023-03-13 2023-04-11 南昌工程学院 一种水下大坝表面裂缝识别方法
CN115953672B (zh) * 2023-03-13 2024-02-27 南昌工程学院 一种水下大坝表面裂缝识别方法

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