TY - JOUR
T1 - Deep Learning for Unmanned Aerial Vehicle-Based Object Detection and Tracking
T2 - A survey
AU - Wu, Xin
AU - Li, Wei
AU - Hong, Danfeng
AU - Tao, Ran
AU - Du, Qian
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2022/3/1
Y1 - 2022/3/1
N2 - Owing to effective and flexible data acquisition, unmanned aerial vehicles (UAVs) have recently become a hotspot across the fields of computer vision (CV) and remote sensing (RS). Inspired by the recent success of deep learning (DL), many advanced object detection and tracking approaches have been widely applied to various UAV-related tasks, such as environmental monitoring, precision agriculture, and traffic management.
AB - Owing to effective and flexible data acquisition, unmanned aerial vehicles (UAVs) have recently become a hotspot across the fields of computer vision (CV) and remote sensing (RS). Inspired by the recent success of deep learning (DL), many advanced object detection and tracking approaches have been widely applied to various UAV-related tasks, such as environmental monitoring, precision agriculture, and traffic management.
UR - http://www.scopus.com/inward/record.url?scp=85118621887&partnerID=8YFLogxK
U2 - 10.1109/MGRS.2021.3115137
DO - 10.1109/MGRS.2021.3115137
M3 - Article
AN - SCOPUS:85118621887
SN - 2473-2397
VL - 10
SP - 91
EP - 124
JO - IEEE Geoscience and Remote Sensing Magazine
JF - IEEE Geoscience and Remote Sensing Magazine
IS - 1
ER -