The Effects of Depth and Altitude on Image-Based Shark Size Measurements Using UAV Surveillance

被引:1
|
作者
Rex, Patrick T. [1 ]
Abbott, Kevin J. [1 ]
Prezgay, Rebecca E. [1 ]
Lowe, Christopher G. [1 ]
机构
[1] Calif State Univ Long Beach, Dept Biol Sci, 1250 Bellflower Blvd, Long Beach, CA 90840 USA
关键词
drones; shark sizing; length measurement; drone-based shark sizing; UNDERWATER; MORTALITY; SURVIVAL; CAUGHT;
D O I
10.3390/drones8100547
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
Drones are an ecological tool used increasingly in shark research over the past decade. Due to their high-resolution camera and GPS systems, they have been used to estimate the sizes of animals using drone-based photogrammetry. Previous studies have used drone altitude to measure the target size accuracy of objects at the surface; however, target depth and its interaction with altitude have not been studied. We used DJI Mavic 3 video (3960 x 2160 pixel) and images (5280 x 3960 pixel) to measure an autonomous underwater vehicle of known size traveling at six progressively deeper depths to assess how sizing accuracy from a drone at 10 m to 80 m altitude is affected. Drone altitudes below 40 m and target depths below 2 m led to an underestimation of size of 76%. We provide evidence that accounting for the drone's altitude and the target depth can significantly increase accuracy to 5% underestimation or less. Methods described in this study can be used to measure free-swimming, submerged shark size with accuracy that rivals hand-measuring methods.
引用
收藏
页数:17
相关论文
共 50 条
  • [31] Automatic Microplot Localization Using UAV Images and a Hierarchical Image-Based Optimization Method
    Mardanisamani, Sara
    Ayalew, Tewodros W.
    Badhon, Minhajul Arifin
    Khan, Nazifa Azam
    Hasnat, Gazi
    Duddu, Hema
    Shirtliffe, Steve
    Vail, Sally
    Stavness, Ian
    Eramian, Mark
    PLANT PHENOMICS, 2021, 2021
  • [32] A Novel Fuzzy Image-Based UAV Landing Using RGBD Data and Visual SLAM
    Sepahvand, Shayan
    Amiri, Niloufar
    Masnavi, Houman
    Mantegh, Iraj
    Janabi-Sharifi, Farrokh
    DRONES, 2024, 8 (10)
  • [33] Autonomous Landing of a VTOL UAV on a Moving Platform Using Image-based Visual Servoing
    Lee, Daewon
    Ryan, Tyler
    Kim, H. Jin.
    2012 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), 2012, : 971 - 976
  • [34] Image-Based Sense and Avoid of Small Scale UAV Using Deep Learning Approach
    Huang, Zong-Ying
    Lai, Ying-Chih
    2020 INTERNATIONAL CONFERENCE ON UNMANNED AIRCRAFT SYSTEMS (ICUAS'20), 2020, : 545 - 550
  • [35] UAV Image-based Forest Fire Detection Approach Using Convolutional Neural Network
    Chen, Yanhong
    Zhang, Youmin
    Xin, Jing
    Wang, Guangyi
    Mu, Lingxia
    Yi, Yingmin
    Liu, Han
    Liu, Ding
    PROCEEDINGS OF THE 2019 14TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA 2019), 2019, : 2118 - 2123
  • [36] Autonomous Landing on Ground Target of UAV by Using Image-Based Visual Servo Control
    Zhang, Yongwei
    Yu, Yangguang
    Jia, Shengde
    Wang, Xiangke
    PROCEEDINGS OF THE 36TH CHINESE CONTROL CONFERENCE (CCC 2017), 2017, : 11204 - 11209
  • [37] Image-based Continuous Displacement Measurements Using an Improved Spectral Approach
    Mortazavi, F.
    Levesque, M.
    Villemure, I.
    STRAIN, 2013, 49 (03) : 233 - 248
  • [38] Enhanced Incremental Image Stitching for Low-Altitude UAV Imagery With Depth Estimation
    Qian, Wei
    Yang, Yusheng
    Xiao, Yao
    Xu, Kang
    Xie, Shaorong
    Xie, Yangmin
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2024, 21
  • [39] The Development of Image-based Algorithm to Identify Altitude Change of a Quadcopter
    Pah, Nemuel Daniel
    Hermawan, Henry
    2015 7th International Conference on Information Technology and Electrical Engineering (ICITEE), 2015, : 76 - 81
  • [40] Current status of image-based surveillance in hepatocellular carcinoma
    Kim, Dong Hwan
    Choi, Joon-Il
    ULTRASONOGRAPHY, 2021, 40 (01) : 45 - 56