CN113358665A - 一种无人机隧洞缺陷检测方法及系统 - Google Patents
一种无人机隧洞缺陷检测方法及系统 Download PDFInfo
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Abstract
本发明涉及一种无人机隧洞缺陷检测方法及系统,无人机上搭载有LED模块、相机、激光雷达、超声波测距仪和IMU,方法包括:基于LED模块和相机在隧洞内采集图像,得到训练图像集;使用训练图像集训练得到缺陷检测模型;采集实时隧洞图像,使用缺陷检测模型对实时隧洞图像进行疑似缺陷检测,基于相机、激光雷达、超声波测距仪和IMU得到无人机位姿信息,控制无人机悬停。与现有技术相比,本发明使用LED模块补充隧洞内光照,融合IMU、相机、激光雷达和超声波测距仪实现无人机位姿估计,使用训练好的缺陷检测模型实时检测是否存在疑似缺陷,发现疑似缺陷后悬停并进一步进行缺陷检测,能够在无GPS信号且内部高度对称的隧洞内实现精确的位姿估计和缺陷检测。
Description
技术领域
本发明涉及无人机巡检领域,尤其是涉及一种无人机隧洞缺陷检测方法及系统。
背景技术
近几年来,我国基础建设设施,如地下地铁隧道、铁路隧道和高速隧道等经过长期使用,设施进入“老龄”期,需要定期进行维护和检查,否则会造成灾难性的后果。然而,传统的检修需要大量人力物力,存在工作量大、危险系数高、工作环境恶劣等问题,因此,急需利用无人机进行设施维护。无人机具有灵活性、敏捷性,可以轻松到达人无法到达的地方。
现有技术中,无人机在巡检过程中自主飞行,使用GPS信号进行定位,实时更新无人机的位置信息,当无人机发现疑似目标区域后,会悬停在该区域,进行进一步的精确作业,并将此时悬停的位置信息和作业结果返回上位机,以便工作人员后续对该位置进行人工勘测,或为工作人员提供参考。
但是,在进行隧洞缺陷检测时,由于隧洞中光线较弱,甚至会出现黑暗区域,因此采集的图像往往质量不高。而且,隧洞中无法使用GPS技术,无人机定位随着时间推移而误差不断累积,由于隧洞是高度对称结构,几乎没有其他几何特征和纹理,这也给无人机在多个自由度的位姿估计带来了挑战。由于定位精度不足,基于图像发现隧洞缺陷后,返回的位置信息也存在很大的误差。因此,现有技术不能满足隧洞缺陷检测要求。
发明内容
本发明的目的就是为了克服上述现有技术存在的缺陷而提供一种无人机隧洞缺陷检测方法及系统,使用LED模块补充隧洞内光照,融合IMU、相机、激光雷达和超声波测距仪实现无人机位姿估计,使用训练好的缺陷检测模型实时检测是否存在疑似缺陷,发现疑似缺陷后悬停并进一步进行缺陷检测,能够在无GPS信号且内部高度对称的隧洞内实现精确的位姿估计和缺陷检测。
本发明的目的可以通过以下技术方案来实现:
一种无人机隧洞缺陷检测方法,所述无人机上搭载有LED模块、相机、激光雷达、超声波测距仪和IMU,包括以下步骤:
S1、在无人机上搭载LED模块和相机,LED模块用于补充隧洞内的光照,基于LED模块和相机在隧洞内采集多张隧洞缺陷原始图像,得到原始图像集,对原始图像集进行预处理,得到训练图像集;
S2、构建神经网络模型,使用训练图像集对神经网络模型进行训练,得到缺陷检测模型;
S3、基于相机、激光雷达、超声波测距仪和IMU得到无人机位姿信息,无人机在隧道内飞行的同时基于LED模块和相机采集实时隧洞图像,并使用缺陷检测模型对实时隧洞图像进行疑似缺陷检测,如果检测到疑似缺陷,则执行步骤S4,否则,无人机继续飞行,重复步骤S3;
S4、无人机悬停,基于LED模块和相机采集疑似缺陷图像,并使用缺陷检测模型对疑似缺陷图像进行缺陷检测,记录无人机位置信息和缺陷检测结果,无人机继续飞行,执行步骤S3。
进一步的,对原始图像集进行预处理包括:使用LED模块进行额外补光时,由于无人机在飞行过程中的抖动,会导致采集到的图像存在亮度不均匀、细节模糊、噪点等问题,因此对原始图像集中的原始图像进行去噪和图像增强处理,提升图像质量,之后标记各个原始图像的缺陷类型,得到训练图像集。
更进一步的,所述缺陷类型包括裂缝缺陷和剥落缺陷,所述裂缝缺陷是指隧洞表面的线性裂缝,所述剥落缺陷是指隧洞表面混凝土脱落后的凹陷。
更进一步的,步骤S1中,对原始图像集进行去噪和图像增强处理后还包括数据增强,数据增强具体为对原始图像集中的图像样本进行翻转、对比度、亮度调节和图像随机裁剪处理,并将数据增强得到的图像加入原始图像集。
进一步的,在不借助GPS信号的情况下,想实现无人机的精准悬停需要获取精确的位置估计,只依靠激光雷达无法在结构重复、高度对称且自相似的隧洞环境中实现定位,本申请基于相机、激光雷达、超声波测距仪和IMU得到无人机位姿信息具体为:
获取IMU测量的无人机运动信息,基于无人机运动信息得到无人机的第一位置信息;
所述相机为双目相机,获取双目相机采集的无人机飞行图像,对无人机飞行图像进行预处理后,使用光流法进行特征点跟踪,解算得到无人机的第二位置信息;
获取激光雷达采集的雷达点云数据,实时构建2D SLAM,将当前获取的雷达点云数据与实时地图进行匹配,从而求解得到无人机的第三位置信息;
基于超声波测距仪测量得到无人机的绝对高度信息;
以第一位置信息为预测值,以第二位置信息、第三位置信息和绝对高度信息作为观测值,融合得到无人机位姿信息。
更进一步的,使用卡尔曼滤波算法UKF将第一位置信息、第二位置信息、第三位置信息和绝对高度信息融合,得到无人机位姿信息。
进一步的,步骤S3和步骤S4中,无人机在飞行过程中还包括避障控制,在所述避障控制中基于人工势能场方法,根据无人机的速度以及无人机与障碍物之间的距离改变无人机的速度。
一种无人机隧洞缺陷检测系统,包括:
无人机,所述无人机上搭载有LED模块、相机、激光雷达、超声波测距仪和IMU;
位置计算模块,基于相机、激光雷达、超声波测距仪和IMU得到无人机位姿信息;
缺陷识别模块,基于LED模块和相机采集的实时隧洞图像和缺陷检测模型进行疑似缺陷检测和缺陷检测;
飞行控制模块,基于飞行过程中无人机位姿信息和缺陷识别模块的疑似缺陷检测结果,控制无人机的飞行和悬停。
进一步的,所述飞行控制模块还进行无人机飞行过程中的避障控制,在所述避障控制中基于人工势能场方法,根据无人机的速度以及无人机与障碍物之间的距离改变无人机的速度。
进一步的,所述缺陷检测系统还包括上位机和通信模块,所述通信模块搭载在无人机上,上位机通过通信模块与无人机隧洞缺陷检测系统通信连接。
与现有技术相比,本发明具有以下有益效果:
(1)使用LED模块补充隧洞内光照,融合IMU、相机、激光雷达和超声波测距仪实现无人机位姿估计,使用训练好的缺陷检测模型实时检测是否存在疑似缺陷,发现疑似缺陷后悬停并进一步进行缺陷检测,能够在无GPS信号且内部高度对称的隧洞内实现精确的位姿估计和缺陷检测。
(2)人工采集隧洞缺陷原始图像,进行图像增强、去噪处理以提升图像质量,通过数据增强扩充了样本多样性,从而提升了缺陷检测模型的检测精度和鲁棒性。
(3)激光雷达无法在结构重复、高度对称且自相似的隧洞环境中实现定位,本申请通过UKF算法融合基于IMU的第一位置信息、基于相机的第二位置信息、基于激光雷达的第三位置信息和基于超声波测距仪的高度信息,得到无人机位姿信息,实现了无人机在隧洞环境下的位置估计。
附图说明
图1为缺陷检测方法的流程图;
图2为确定无人机位姿信息的流程图;
图3为隧洞缺陷检测系统的框图。
具体实施方式
下面结合附图和具体实施例对本发明进行详细说明。本实施例以本发明技术方案为前提进行实施,给出了详细的实施方式和具体的操作过程,但本发明的保护范围不限于下述的实施例。
实施例1:
本实施例中,进行隧洞缺陷检测的无人机为多旋翼无人机,其上搭载有LED模块、相机、激光雷达、超声波测距仪和惯性测量装置IMU,LED模块用于补充隧洞内的光照,使得相机能在一定亮度下进行图像采集,激光雷达采集的雷达点云数据能够实现无人机的定位和避障,相机采集隧洞图像,以进行缺陷检测,同时能够作为视觉里程计来估算无人机的姿态信息,超声波测距仪用于获取无人机的高度信息。
一种无人机隧洞缺陷检测方法,如图1所示,包括以下步骤:
S1、在无人机上搭载LED模块和相机,基于LED模块和相机在隧洞内采集多张隧洞缺陷原始图像,得到原始图像集,对原始图像集进行预处理,得到训练图像集;
S2、构建神经网络模型,使用训练图像集对神经网络模型进行训练,得到缺陷检测模型;
S3、基于相机、激光雷达、超声波测距仪和IMU得到无人机位姿信息,无人机在隧道内飞行的同时基于LED模块和相机采集实时隧洞图像,并使用缺陷检测模型对实时隧洞图像进行疑似缺陷检测,如果检测到疑似缺陷,则执行步骤S4,否则,无人机继续飞行,重复步骤S3;
S4、无人机悬停,基于LED模块和相机采集疑似缺陷图像,并使用缺陷检测模型对疑似缺陷图像进行缺陷检测,记录无人机位置信息和缺陷检测结果,无人机继续飞行,执行步骤S3。
简单来讲,方法包括两个阶段,第一阶段为缺陷检测模型建立阶段,第二阶段为缺陷检测阶段。
在缺陷检测模型建立阶段中,可以人工控制无人机在隧洞内飞行,飞行的由相机采集隧洞的图像,工作人员观察图像,如果发现其上存在缺陷,则控制无人机悬停,采集多张包含缺陷的隧洞缺陷原始图像,之后继续控制无人机飞行,重复上述过程,得到原始图像集。
缺陷类型包括裂缝缺陷和剥落缺陷,裂缝缺陷是指隧洞表面的线性裂缝,剥落缺陷是指隧洞表面硬化混凝土的掉落,留下类似圆形或椭圆形的凹陷。
使用LED模块进行额外补光时,由于无人机在飞行过程中的抖动,会导致采集到的图像存在亮度不均匀、细节模糊、噪点等问题,因此对原始图像集中的原始图像进行去噪和图像增强处理,如对比度拉伸等操作,提升图像质量,之后标记各个原始图像的缺陷类型,得到训练图像集。
由于采集到的原始图像可能数量较少,可以通过数据增强扩充训练图像集,对原始图像集进行去噪和图像增强处理后还包括数据增强,数据增强具体为对原始图像集中的图像样本进行翻转、对比度、亮度调节和图像随机裁剪处理,并将数据增强得到的图像加入原始图像集。
之后构建神经网络,可以采用CNN卷积神经网络,或者其他神经网络结构,基于训练图像集进行训练,当识别精度达到预期阈值时,就得到训练好的缺陷检测模型。
本实施例中,将缺陷检测模型存储在无人机上搭载的小型迷你电脑NUC中,完成缺陷检测模型建立阶段。
在缺陷检测阶段中,无人机自主在隧洞内飞行,使用缺陷检测模型对飞行过程中采集的实时隧洞图像进行缺陷检测,如果检测到疑似缺陷,则控制无人机在疑似缺陷位置悬停,采集质量更高的图像,再次基于缺陷检测模型进行缺陷检测。
在不借助GPS信号的情况下,想实现无人机的精准悬停需要获取精确的位置估计,只依靠激光雷达无法在结构重复、高度对称且自相似的隧洞环境中实现定位,如图2所示,本申请基于相机、激光雷达、超声波测距仪和IMU得到无人机位姿信息具体为:
获取IMU测量的无人机运动信息,基于无人机运动信息得到无人机的第一位置信息;
相机为双目相机,获取双目相机采集的无人机飞行图像,对无人机飞行图像进行预处理后,使用光流法进行特征点跟踪,解算得到无人机的第二位置信息;
双目相机获取飞行时图像,其帧率为30FPS;获取飞行过程中左右两侧的图像;先使用直方图均衡增强两幅图像的图像对比度,再使用自适应阈值法分割图像,获得黑白图像,检测其中的Fast特征点,使用KLT光流对特征点进行跟踪,利用RANSAC算法对跟踪错误的点进行剔除,对左右两图的光流跟踪获取特征点深度:
其中,B为左右相机的基线长度,fx为焦距,d为视差;
当获取图片达到一定数量时,采用PnP算法对相邻时刻的特征进行解算获取无人机位姿信息[xw,yw,depth]。
获取激光雷达采集的雷达点云数据,实时构建2D SLAM,将当前获取的雷达点云数据与实时地图进行匹配,从而求解得到无人机的第三位置信息;
激光雷达扫描周边环境,实时构建2D SLAM,使用Hector-SLAM算法在激光雷达原始数据和当前构建的地图匹配时,使用高斯-牛顿方法求解,得到该时刻无人机在地图中概率最大的位姿;
其中,ξ=(x,y,θ)T表示位姿信息,Si(ξ)表示激光雷达端点坐标在世界坐标系下的表示,M(Si(ξ))表示坐标点Si(ξ)的地图值。
基于超声波测距仪测量得到无人机的绝对高度信息;
无人机在飞行过程中还包括避障控制,在避障控制中基于人工势能场方法,根据无人机的速度以及无人机与障碍物之间的距离改变无人机的速度。在飞行过程中,基于激光雷达扫描障碍物信息,假定障碍物是一个正电荷,无人机也是正电荷,无人机与障碍物之间会产生排斥力,且距离越近,排斥力越大,排斥力可以用无人机当前位置与障碍物之间距离的平方表示。当无人机接近障碍物时,无人机的速度就会被降低,使得无人机与障碍物之间的距离不会过近,从而实现避障控制。
本申请还提供一种无人机隧洞缺陷检测系统,如图3所示,包括:
无人机,无人机上搭载有LED模块、相机、激光雷达、超声波测距仪和IMU;
位置计算模块,基于相机、激光雷达、超声波测距仪和IMU得到无人机位姿信息;
缺陷识别模块,基于LED模块和相机采集的实时隧洞图像和缺陷检测模型进行疑似缺陷检测和缺陷检测;
飞行控制模块,基于飞行过程中无人机位姿信息和缺陷识别模块的疑似缺陷检测结果,控制无人机的飞行和悬停。
飞行控制模块还进行无人机飞行过程中的避障控制,在避障控制中基于人工势能场方法,根据无人机的速度以及无人机与障碍物之间的距离改变无人机的速度。
缺陷检测系统还包括上位机和通信模块,通信模块搭载在无人机上,上位机通过通信模块与无人机隧洞缺陷检测系统通信连接。
具体的,在执行隧洞缺陷检测任务时,上位机即为工作人员的电脑,在无人机上搭载小型迷你电脑NUC,NUC中存储缺陷检测模型,位置计算模块、缺陷识别模块、飞行控制模块均集成在NUC中,通信模块能够实现无人机与上位机之间的实时通信连接,工作人员可以接受无人机返回的缺陷检测信息、位置信息、无人机位姿信息等,也可以向无人机发送控制指令。
以上详细描述了本发明的较佳具体实施例。应当理解,本领域的普通技术人员无需创造性劳动就可以根据本发明的构思作出诸多修改和变化。因此,凡本技术领域中技术人员依本发明的构思在现有技术的基础上通过逻辑分析、推理或者有限的实验可以得到的技术方案,皆应在由权利要求书所确定的保护范围内。
Claims (10)
1.一种无人机隧洞缺陷检测方法,其特征在于,所述无人机上搭载有LED模块、相机、激光雷达、超声波测距仪和IMU,包括以下步骤:
S1、在无人机上搭载LED模块和相机,基于LED模块和相机在隧洞内采集多张隧洞缺陷原始图像,得到原始图像集,对原始图像集进行预处理,得到训练图像集;
S2、构建神经网络模型,使用训练图像集对神经网络模型进行训练,得到缺陷检测模型;
S3、基于相机、激光雷达、超声波测距仪和IMU得到无人机位姿信息,无人机在隧道内飞行的同时基于LED模块和相机采集实时隧洞图像,并使用缺陷检测模型对实时隧洞图像进行疑似缺陷检测,如果检测到疑似缺陷,则执行步骤S4,否则,无人机继续飞行,重复步骤S3;
S4、无人机悬停,基于LED模块和相机采集疑似缺陷图像,并使用缺陷检测模型对疑似缺陷图像进行缺陷检测,记录无人机位置信息和缺陷检测结果,无人机继续飞行,执行步骤S3。
2.根据权利要求1所述的一种无人机隧洞缺陷检测方法,其特征在于,对原始图像集进行预处理包括:对原始图像集中的原始图像进行去噪和图像增强处理,标记各个原始图像的缺陷类型,得到训练图像集。
3.根据权利要求2所述的一种无人机隧洞缺陷检测方法,其特征在于,所述缺陷类型包括裂缝缺陷和剥落缺陷,所述裂缝缺陷是指隧洞表面的线性裂缝,所述剥落缺陷是指隧洞表面混凝土脱落后的凹陷。
4.根据权利要求2所述的一种无人机隧洞缺陷检测方法,其特征在于,步骤S1中,对原始图像集进行去噪和图像增强处理后还包括数据增强,数据增强具体为对原始图像集中的图像样本进行翻转、对比度、亮度调节和图像随机裁剪处理,并将数据增强得到的图像加入原始图像集。
5.根据权利要求1所述的一种无人机隧洞缺陷检测方法,其特征在于,步骤S3中,基于相机、激光雷达、超声波测距仪和IMU得到无人机位姿信息具体为:
获取IMU测量的无人机运动信息,基于无人机运动信息得到无人机的第一位置信息;
所述相机为双目相机,获取双目相机采集的无人机飞行图像,对无人机飞行图像进行预处理后,使用光流法进行特征点跟踪,解算得到无人机的第二位置信息;
获取激光雷达采集的雷达点云数据,基于雷达点云数据求解得到无人机的第三位置信息;
基于超声波测距仪测量得到无人机的绝对高度信息;
以第一位置信息为预测值,以第二位置信息、第三位置信息和绝对高度信息作为观测值,融合得到无人机位姿信息。
6.根据权利要求5所述的一种无人机隧洞缺陷检测方法,其特征在于,使用卡尔曼滤波算法UKF将第一位置信息、第二位置信息、第三位置信息和绝对高度信息融合,得到无人机位姿信息。
7.根据权利要求1所述的一种无人机隧洞缺陷检测方法,其特征在于,步骤S3和步骤S4中,无人机在飞行过程中还包括避障控制,在所述避障控制中基于人工势能场方法,根据无人机的速度以及无人机与障碍物之间的距离改变无人机的速度。
8.一种无人机隧洞缺陷检测系统,其特征在于,基于如权利要求1-7中任一所述的无人机隧洞缺陷检测方法,包括:
无人机,所述无人机上搭载有LED模块、相机、激光雷达、超声波测距仪和IMU;
位置计算模块,基于相机、激光雷达、超声波测距仪和IMU得到无人机位姿信息;
缺陷识别模块,基于LED模块和相机采集的实时隧洞图像和缺陷检测模型进行疑似缺陷检测和缺陷检测;
飞行控制模块,基于飞行过程中无人机位姿信息和缺陷识别模块的疑似缺陷检测结果,控制无人机的飞行和悬停。
9.根据权利要求8所述的一种无人机隧洞缺陷检测系统,其特征在于,所述飞行控制模块还进行无人机飞行过程中的避障控制,在所述避障控制中基于人工势能场方法,根据无人机的速度以及无人机与障碍物之间的距离改变无人机的速度。
10.根据权利要求8所述的一种无人机隧洞缺陷检测系统,其特征在于,所述缺陷检测系统还包括上位机和通信模块,所述通信模块搭载在无人机上,上位机通过通信模块与无人机隧洞缺陷检测系统通信连接。
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