Flight Path Setting and Data Quality Assessments for Unmanned-Aerial-Vehicle-Based Photogrammetric Bridge Deck Documentation

被引:0
|
作者
Chen, Siyuan [1 ,2 ]
Zeng, Xiangding [3 ]
Laefer, Debra F. [2 ,4 ]
Truong-Hong, Linh [5 ]
Mangina, Eleni [6 ]
机构
[1] Hunan Inst Sci & Technol, Sch Informat Sci & Engn, Yueyang 414015, Peoples R China
[2] Univ Coll Dublin, Sch Civil Engn, Dublin D04 C1P1, Ireland
[3] Hunan Inst Sci & Technol, Coll Mech Engn, Yueyang 414015, Peoples R China
[4] NYU, Ctr Urban Sci & Progress, Tandon Sch Engn, New York, NY 10012 USA
[5] Delft Univ Technol, Sch Civil Engn, NL-2628 CD Delft, Netherlands
[6] Univ Coll Dublin, Sch Comp Sci, Dublin D04 C1P1, Ireland
关键词
UAV; SFM; photogrammetry; point cloud; quality evaluation; IMAGE CORRELATION; UAV; POINT; SFM; MODELS; LIDAR;
D O I
10.3390/s23167159
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Imagery from Unmanned Aerial Vehicles can be used to generate three-dimensional (3D) point cloud models. However, final data quality is impacted by the flight altitude, camera angle, overlap rate, and data processing strategies. Typically, both overview images and redundant close-range images are collected, which significantly increases the data collection and processing time. To investigate the relationship between input resources and output quality, a suite of seven metrics is proposed including total points, average point density, uniformity, yield rate, coverage, geometry accuracy, and time efficiency. When applied in the field to a full-scale structure, the UAV altitude and camera angle most strongly affected data density and uniformity. A 66% overlapping was needed for successful 3D reconstruction. Conducting multiple flight paths improved local geometric accuracy better than increasing the overlapping rate. The highest coverage was achieved at 77% due to the formation of semi-irregular gridded gaps between point groups as an artefact of the Structure from Motion process. No single set of flight parameters was optimal for every data collection goal. Hence, understanding flight path parameter impacts is crucial to optimal UAV data collection.
引用
收藏
页数:29
相关论文
共 26 条
  • [1] Quality evaluation model of unmanned aerial vehicle's horizontal flight maneuver based on flight data
    Teng H.
    Li B.
    Gao Y.
    Yang D.
    Zhang Y.
    Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics, 2019, 45 (10): : 2108 - 2114
  • [2] Automated Unmanned Aerial Vehicle-Based Bridge Deck Delamination Detection and Quantification
    Zhang, Qianyun
    Ro, Sun Ho
    Wan, Zhe
    Babanajad, Saeed
    Braley, John
    Barri, Kaveh
    Alavi, Amir H.
    TRANSPORTATION RESEARCH RECORD, 2023, 2677 (08) : 24 - 36
  • [3] A General Method for Pre-Flight Preparation in Data Collection for Unmanned Aerial Vehicle-Based Bridge Inspection
    Almasi, Pouya
    Xiao, Yangjian
    Premadasa, Roshira
    Boyle, Jonathan
    Jauregui, David
    Wan, Zhe
    Zhang, Qianyun
    DRONES, 2024, 8 (08)
  • [4] Unmanned Aerial Vehicle Flight Point Classification Algorithm Based on Symmetric Big Data
    Kwak, Jeonghoon
    Park, Jong Hyuk
    Sung, Yunsick
    SYMMETRY-BASEL, 2017, 9 (01):
  • [5] A Quality Assessment Model for Unmanned Aerial Vehicle Path Planning Based on Bayesian Networks
    Xia, Hong-xia
    Miao, Yong-fei
    Chen, Yan-en
    Luo, Rui-qi
    2015 12TH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (FSKD), 2015, : 849 - 853
  • [6] Sensing Image Data Based Unmanned Aerial Vehicle Channel Path Loss Prediction
    Sun, Mingran
    Huang, Ziwei
    Bai, Lu
    Cheng, Xiang
    Zhang, Hongguang
    Feng, Tao
    Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence, 2023, 36 (11): : 987 - 996
  • [7] An Anti-interference Method for About Unmanned Aerial Vehicle Flight Data Based On VxWorks
    Li Bo
    Zhang Shengbing
    Yang Junpeng
    Wang Liang
    2016 IEEE CHINESE GUIDANCE, NAVIGATION AND CONTROL CONFERENCE (CGNCC), 2016, : 7 - 9
  • [8] Unmanned Aerial Vehicle Flight Data Anomaly Detection Based on Multirate-Aware LSTM
    Lu, Hui
    Wang, Zan
    Shi, Yuhui
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2024, 73
  • [9] Task-Driven Path Planning for Unmanned Aerial Vehicle-Based Bridge Inspection in Wind Fields
    Wang, Yonghu
    Duan, Chengcheng
    Huang, Xinyu
    Zhao, Juan
    Zheng, Ran
    Li, Haiping
    FLUIDS, 2023, 8 (12)
  • [10] Quality-Oriented Hybrid Path Planning Based on A* and Q-Learning for Unmanned Aerial Vehicle
    Li, Dongcheng
    Yin, Wangping
    Wong, W. Eric
    Jian, Mingyong
    Chau, Matthew
    IEEE ACCESS, 2022, 10 : 7664 - 7674