基于无人机与数字图像法的混凝土结构表面裂缝检测应用研究

对于经常受到振动、疲劳、地基沉降、冻融循环等因素影响的高耸结构、桥梁高墩、高层建筑等,结构裂缝等混凝土表面缺陷非常常见并需要长期观测。通过无人机对结构表面拍照并应用图像算法识别裂缝等缺陷特征可以很好的解决人工检测困难的问题。本文以标准裂缝宽度卡作为参照,使用相机从各方位对模拟裂缝进行拍照,模拟实际工程中无人机拍摄的状态。实现对裂缝的长度、宽度、方向等几何参数进行测量,并应用数字图像法对带裂缝混凝土结构图像进行识别,证明无人机拍摄图像分析相较传统人工检测的优越性。     

结构表面裂缝数字图像法识别研究综述与前景展望

裂缝反映结构受力状态与安全性、耐久性,是结构现场安全性检测监测以及结构模型试验研究的重要指标之一。现有的人工裂缝识别技术难以满足工程现场与实验室需求,操作中存在测不准、高空多、效率低、记不全等缺点。相比之下,采用数字图像法进行结构表面裂缝识别,具有便捷、自动、定量、准确等优势。文章对结构表面裂缝数字图像法识别研究进行系统综述,对裂缝识别中图像预处理、裂缝识别与提取、裂缝参数计算等重要环节的常见算法进行讨论,阐述采用多视角几何三维重建方法实现裂缝成像修正与拼接、裂缝表达输出的原理与流程,结合实桥案例报道了基于无人机平台的裂缝识别研究与应用,讨论国内首部数字图像法检测规程《工程结构数字图像法检测技术规程》征求意见稿中裂缝检测的相关规定。最后,对结构表面裂缝数字图像法识别研究进行前景展望。     

基于几何成像原理和图传系统的UAV桥检技术

基于影像几何原理,推导了无人机(UAV)进行桥梁检测中物高、物距、焦距和像高之间的关系。提出了适用于桥梁检测的无人机图像传输系统。通过对照试验分别探究相机的测量精度和测量距离之间的关系,测量精度和测量角度之间的关系,以及测量精度和本身裂缝大小之间的关系。经测试,文中提出的无人机图像系统能够满足桥梁裂缝测量的要求。     

基于无人机摄影测量的房屋裂缝智能检测技术

基于计算机视觉,提出通过无人机两点激光测距自动提取裂缝几何参数的方法,构建基于坐标变换的图像像素解析度计算方法,建立图像处理驱动的裂缝长度、宽度、走向等自动提取方法。对在役建筑物外立面裂缝进行检测,结果表明,基于无人机摄影测量的裂缝检测方法具有良好的精度及工程应用价值。     

基于O-YOLOv3 的无人机森林火灾识别研究

为解决传统森林火灾检测误报率高、响应速度慢等问题,提出了以无人机作为探测平台,地面站作为火灾识别系统,实现森林火灾的自动探测、识别和定位。开发了六旋翼无人机平台,通过所搭载的红外摄像机和机载计算机获取森林火灾现场图像并实时传回地面。利用地面站对所接收到的火灾图像进行处理,实现对森林火场的在线监测。在森林火灾识别算法方面,提出了O_YOLOv3 算法,采用Darknet 框架进行网络训练,使用K_means 方法自动生成锚点,有效提高火灾识别精度与响应速度。将O_YOLOv3 算法与其他几种算法进行对比实验验证本文算法的有效性。实验结果表明：O_YOLOv3 火灾识别算法能够快速、精准识别森林火灾;所研制的基于O_YOLOv3 的无人机森林火灾探测系统能够用于实际森林火灾探测。     

盾构隧道管片裂缝图像识别及宽度计算优化方法

针对盾构隧道管片结构裂损的图像,基于数字图像处理技术,运用软件MATLAB的图像处理功能进行裂缝图像灰度变换及均衡化、图像分割及裂缝识别、边缘提取和宽度计算。在宽度计算中,采用霍夫变换对曲线裂缝进行拟合,细化裂缝图像的宽度,得到更精确信息。     

基于全卷积神经网络的桥梁裂缝分割和测量方法

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。     

基于计算机视觉技术的结构表面裂缝检测方法研究

计算机视觉技术用于混凝土结构表面裂缝检测,具有现场检测方便、效率高、客观性强的特点,但图像数据分析是该技术的核心,其中裂缝提取与定量测量较为复杂。为提高裂缝图像处理效率和准确率,将深度学习和数字图像处理技术相结合,提出一种裂缝检测方法。建立基于深度卷积神经网络的裂缝识别模型,在图像上自动定位裂缝并结合图像局域阈值分割方法提取裂缝。在裂缝宽度定量测量方面,采用双边滤波算法和三段线性变换对裂缝图像进行预处理,提高了裂缝边缘识别的精确度。通过改进边缘梯度法,实现裂缝最大宽度的定位和裂缝最大宽度的自动获取。该研究为全自动识别裂缝图像及高精度测量裂缝宽度提供了一种解决方法。     

无人机在沿海腐蚀环境桥梁病害巡检中的应用

无人机技术已逐步应用于桥梁的病害检测中,本文针对服役于沿海腐蚀环境中桥梁对混凝土耐久性较高的特点,特别是混凝土裂缝宽度限制、防腐涂料开裂等,采用无人机对上述病害进行了定量化检测的应用。特别是针对混凝土裂缝宽度量测,本文利用无人机进行了裂缝宽度量测尺的设置,实现了裂缝宽度的精确测量。研究表明,无人机检测技术相比传统方法可显著扩大巡检范围,同时便利性、安全性、经济性等方面也优势明显。     

基于图像处理技术的房屋裂缝宽度变化实时监测研究

基于MATLAB数字图像处理技术,对房屋裂缝宽度变化进行实时监测,依次对采集裂缝图像进行灰度化、图像滤波、二值化、目标区域分割标记和像素标定等处理,计算获得图像裂缝最大宽度值及单次变化值、人工标志间距离变化值。通过与实测数据相比,运用本文提出的计算方法获得房屋裂缝检测数据的识别精度为99.19%。利用MATLAB图像处理技术获得人工标志间距离变化值与最大裂缝宽度变化值累计相对误差为0.16%,人工标志间距离变化能够反映裂缝宽度变化情况。对于实际情况中无法寻找裂缝最大宽度处,而进行裂缝宽度变化实时监测时,可用人工标志间距离变化反映裂缝宽度变化情况。     

Image‐based crack assessment of bridge piers using unmanned aerial vehicles and three‐dimensional scene reconstruction

Crack assessment of bridge piers using unmanned aerial vehicles (UAVs) eliminates unsafe factors of manual inspection and provides a potential way for the maintenance of transportation infrastructures. However, the implementation of UAV‐based crack assessment for real bridge piers is hindered by several key issues, including the following: (a) both perspective distortion and the geometry distortion by nonflat structural surfaces usually appear on crack images taken by the UAV system from the pier surface; however, these two kinds of distortions are difficult to correct at the same time; and (b) the crack image taken by a close‐range inspection flight UAV system is partially imaged, containing only a small part of the entire surface of the pier, and thereby hinders crack localization. In this paper, a new image‐based crack assessment methodology for bridge piers using UAV and three‐dimensional (3D) scene reconstruction is proposed. First, the data acquisition of UAV‐based crack assessment is discussed, and the UAV flight path and photography strategy for bridge pier assessment are proposed. Second, image‐based crack detection and 3D reconstruction are conducted to obtain crack width feature pair sequences and 3D surface models, respectively. Third, a new method of projecting cracks onto a meshed 3D surface triangular model is proposed, which can correct both the perspective distortion and geometry distortion by nonflat structural surfaces, and realize the crack localization. Field test investigations of crack assessment of a real bridge pier using a UAV are carried out for illustration, validation, and error analysis of the proposed methodology.     

Detecting and tracking vehicles in traffic by unmanned aerial vehicles

Using unmanned aerial vehicles (UAV) as devices for traffic data collection exhibits many advantages in collecting traffic information. This paper introduces a new vehicle detecting and tracking system based on image data collected by UAV. This system uses consecutive frames to generate vehicle's dynamic information, such as positions and velocities. Four major modules have been developed: image registration, image feature extraction, vehicle shape detecting, and vehicle tracking. Some unique features have been introduced into this system to customize the vehicle and traffic flow and to jointly use them in multiple consecutive images to increase the system accuracy of detecting and tracking vehicles. Field tests demonstrate that the present system exhibits high accuracy in traffic information acquisition at different UAV altitudes with different view scopes, which can be used in future traffic monitoring and control in metropolitan areas.     

Unmanned aerial vehicle inspection of the Placer River Trail Bridge through image-based 3D modelling

Unmanned aerial vehicles (UAV) are now a viable option for augmenting bridge inspections. Utilising an integrated combination of a UAV and computer vision can decrease costs, expedite inspections and facilitate bridge access. Any such inspection must consider the design of the UAV, the choice of cameras, data acquisition, geometrical resolution, safety regulations and pilot protocols. The Placer River Trail Bridge in Alaska recently served as a test bed for a UAV inspection methodology that integrates these considerations. The end goal was to produce a three-dimensional (3D) model of the bridge using UAV-captured images and a hierarchical Dense Structure-from-Motion algorithm. To maximise the quality of the model and its benefits to inspectors, this goal guided UAV design and mission planning. The resulting inspection methodology integrates UAV design, data capture and data analysis together to provide an optimised 3D model. This model provides inspection documentation while enabling the monitoring of defects. The developed methodology is presented herein, as well as analyses of the 3D models. The results are compared against models generated through laser scanning. The findings demonstrate that the UAV inspection methodology provided superior 3D models with the accuracy to resolve defects and support the needs of infrastructure managers.     

无人机数码航摄数据获取与应用分析

无人机数码航摄相比于传统航空摄影技术,具有机动灵活、成本低廉及生产周期短等优点,是获取小区域基础地理信息数据技术手段的有力补充。本文首先描述了无人机数码航摄系统的技术特点及组成,其次详细地阐述了无人机数码航摄数据获取的技术流程,最后重点分析了无人机数码航摄技术应用现状及前景。     

Autonomous UAVs for Structural Health Monitoring Using Deep Learning and an Ultrasonic Beacon System with Geo‐Tagging

Visual inspection has traditionally been used for structural health monitoring. However, assessments conducted by trained inspectors or using contact sensors on structures for monitoring are costly and inefficient because of the number of inspectors and sensors required. To date, data acquisition using unmanned aerial vehicles (UAVs) equipped with cameras has become popular, but UAVs require skilled pilots or a global positioning system (GPS) for autonomous flight. Unfortunately, GPS cannot be used by a UAV for autonomous flight near some parts of certain structures (e.g., beneath a bridge), but these are the critical locations that should be inspected to monitor and maintain structural health. To address this difficulty, this article proposes an autonomous UAV method using ultrasonic beacons to replace the role of GPS, a deep convolutional neural network (CNN) for damage detection, and a geo‐tagging method for the localization of damage. Concrete cracks, as an example of structural damage, were successfully detected with 97.7% specificity and 91.9% sensitivity, by processing video data collected from an autonomous UAV.     

Anchor-adaptive railway track detection from unmanned aerial vehicle images

Autonomous railway inspection with unmanned aerial vehicles (UAVs) has huge advantages over traditional inspection methods. As a prerequisite for UAV-based autonomous following of railway lines, it is quite essential to develop intelligent railway track detection algorithms. However, there are no existing algorithms currently that can efficiently adapt to the demand for the various forms and changing inclination angles of railway tracks in the UAV aerial images. To address the challenge, this paper proposes a novel anchor-adaptive railway track detection network (ARTNet), which constructs a dual-branch architecture based on projection length discrimination to realize full-angle railway track detection for the UAV aerial images taken from arbitrary viewing angles. Considering the potential capacity imbalance of the two branches that can be caused by the uneven distribution of railway tracks in the dataset, a balanced transpose co-training strategy is proposed to train the two branches coordinately. Moreover, an extra customized transposed consistency loss is designed to guide the training of the network without increasing any computational complexity. A set of experiments have been conducted to verify the feasibility and superiority of the ARTNet. It is demonstrated that our approach can effectively realize full-angle railway track detection and outperform other popular algorithms greatly in terms of both detection accuracy and reasoning efficiency. ARTNet can achieve a mean F1 of 76.12 and run at a speed of 50 more frames per second.     

Noncontact cable force estimation with unmanned aerial vehicle and computer vision

Cables and hangers are critical components of long‐span bridges, tension forces of them are needed to be accurately measured for ensuring the safety of bridges. Traditionally, cable tension forces are measured by attached accelerometers or elastomagnetic (EM) sensors, however, applying these sensors into engineering practice are time‐consuming, labor‐intensive, and highly dangerous. To address these problems, an unmanned aerial vehicle (UAV)‐based noncontact cable force estimation method with computer vision technologies was proposed in this article. Basic concept of the proposed method is to use the UAV‐installed camera for capturing vibration images of cables from a certain distance and cable dynamic properties are extracted by analyzing captured images. It includes two aspects: (a) a line segments detector (LSD) was employed for detecting cable edges from captured video and a line matching algorithm was further proposed for extracting dynamic displacements; (b) the frequency difference of adjacent higher modal frequencies identified from relative displacements of the cable was employed for cable force calculation to avoid the difficulty of extracting fundamental frequency from UAV‐captured video. It should be noted that relative displacement herein refers to the difference between displacements of two points on the same cable. Advantages of the proposed method lie in that the proposed LSD and matching algorithm are more robust than traditional correlation‐based algorithm for calculating dynamic displacements of bridge cables and it does not need to adjust predefined parameters (i.e., subset size in correlation‐based algorithms). In addition, the combination of relative displacement and frequency difference‐based cable force estimation has the capability of enhancing the Fourier spectrum magnitude of bridge cables and reducing the effect of UAV motion on extraction of cable vibration frequencies. The effectiveness and robustness of the proposed method was verified by using an experimental inclined cable and field‐testing data of a long‐span suspension bridge. Results show that calculated cable forces with UAV technology have a good agreement with reference values measured by attached accelerometers and fixed camera, demonstrating correctness and robustness of the proposed method for cable force estimation.     

基于无人机低空航拍航测技术的传统聚落 调查及其应用前景——以南京市老门东为例

通过无人机低空航拍航测技术,为传统聚落的调查与保护提供数字化资料。在反思新时期传统聚落数据资料、调查方法和技术问题的基础上,分析了无人机应用于传统聚落调查的优势及应用前景。以南京市老门东传统聚落为研究对象,通过无人机低空航拍航测及Pix4Dmapper数据处理软件,获取了老门东的正射影像图、数字高程图及数字地表模型。以案例为支撑,将无人机航拍航测技术与传统田野调查法相比较,就调查方式、精度效果、调查成果及运用价值展开具体分析。最后,从技术发展、政策保障、部门支持和学科建设4个方面对无人机航拍航测技术的推广普及提出了相应建议。     

无人机航摄在城市规划建设中的应用

无人机航拍摄影是新兴的航空遥感技术,本文主要从其优于传统航空摄影测量的特点说明使用无人机进行城市规划建设,在实践中取得的明显成效和经验。以无人机平台的微型航空遥感技术,为中小城市特别是镇、县、乡等地区经济和文化建设提供了有效的遥感技术服务手段,适应国家经济和文化建设发展的需要。