Automated Detection of Macrobenthos in Tidal Flats Using Unmanned Aerial Vehicles and Deep Learning

被引:0
|
作者
Dong-Woo, Kim [1 ]
Seung-Woo, Son [1 ]
Sang-Hyuk, Lee [1 ]
Jeongho, Yoon [1 ]
机构
[1] Korea Environment Institute, Water and Land Research Group, Sejong-si,30147, Korea, Republic of
关键词
Compilation and indexing terms; Copyright 2024 Elsevier Inc;
D O I
暂无
中图分类号
学科分类号
摘要
引用
收藏
页码:6251 / 6253
相关论文
共 50 条
  • [31] Evaluation of Reinforcement and Deep Learning Algorithms in Controlling Unmanned Aerial Vehicles
    Jembre, Yalew Zelalem
    Nugroho, Yuniarto Wimbo
    Khan, Muhammad Toaha Raza
    Attique, Muhammad
    Paul, Rajib
    Shah, Syed Hassan Ahmed
    Kim, Beomjoon
    APPLIED SCIENCES-BASEL, 2021, 11 (16):
  • [32] Object Detection and Trajectory Prediction of Unmanned Aerial Vehicle Using Deep Learning
    Aote, Shailendra S.
    Panpaliya, Samiksha
    Hedaoo, Nilanshu
    Mane, Shantanu
    Pathak, Sagar
    SMART TRENDS IN COMPUTING AND COMMUNICATIONS, VOL 2, SMARTCOM 2024, 2024, 946 : 225 - 235
  • [33] Controlling Tiltrotors Unmanned Aerial Vehicles (UAVs) with Deep Reinforcement Learning
    de Almeida, Aline Gabriel
    Colombini, Esther Luna
    Simoes, Alexandre da Silva
    2023 LATIN AMERICAN ROBOTICS SYMPOSIUM, LARS, 2023 BRAZILIAN SYMPOSIUM ON ROBOTICS, SBR, AND 2023 WORKSHOP ON ROBOTICS IN EDUCATION, WRE, 2023, : 107 - 112
  • [34] Automatic Modulation Classification Based on Deep Learning for Unmanned Aerial Vehicles
    Zhang, Duona
    Ding, Wenrui
    Zhang, Baochang
    Xie, Chunyu
    Li, Hongguang
    Liu, Chunhui
    Han, Jungong
    SENSORS, 2018, 18 (03)
  • [35] Optimal Deep Learning Enabled Communication System for Unmanned Aerial Vehicles
    Hilal, Anwer Mustafa
    Alzahrani, Jaber S.
    Elkamchouchi, Dalia H.
    Eltahir, Majdy M.
    Almasoud, Ahmed S.
    Motwakel, Abdelwahed
    Zamani, Abu Sarwar
    Yaseen, Ishfaq
    COMPUTER SYSTEMS SCIENCE AND ENGINEERING, 2023, 45 (01): : 955 - 969
  • [36] Investigation of Unsafe Construction Site Conditions Using Deep Learning Algorithms Using Unmanned Aerial Vehicles
    Kumar, Sourav
    Poyyamozhi, Mukilan
    Murugesan, Balasubramanian
    Rajamanickam, Narayanamoorthi
    Alroobaea, Roobaea
    Nureldeen, Waleed
    SENSORS, 2024, 24 (20)
  • [37] HYPERSALINE TIDAL FLATS DETECTION USING DEEP LEARNING OVER 37 YEARS OF LANDSAT DATA
    Pinheiro, Maria Luize
    Cortinhas, Luiz
    Diniz, Cesar
    Maretto, Raian V.
    Grellert, Mateus
    IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2023, : 3337 - 3340
  • [38] Online Deep Learning for Improved Trajectory Tracking of Unmanned Aerial Vehicles Using Expert Knowledge
    Sarabakha, Andriy
    Kayacan, Erdal
    2019 INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), 2019, : 7727 - 7733
  • [39] Review of Deep Learning Algorithms for Urban Remote Sensing Using Unmanned Aerial Vehicles (UAVs)
    Datta S.
    Durairaj S.
    Recent Advances in Computer Science and Communications, 2024, 17 (02) : 66 - 77
  • [40] Worker Safety and Health Activity Monitoring in Construction Using Unmanned Aerial Vehicles and Deep Learning
    Awolusi, Ibukun
    Akinsemoyin, Aliu
    Chakraborty, Debaditya
    Al-Bayati, Ahmed
    CONSTRUCTION RESEARCH CONGRESS 2022: COMPUTER APPLICATIONS, AUTOMATION, AND DATA ANALYTICS, 2022, : 463 - 473