Deep Learning for Unmanned Aerial Vehicle-Based Object Detection and Tracking: A survey

Xin Wu; Wei Li; Danfeng Hong; Ran Tao; Qian Du

doi:10.1109/MGRS.2021.3115137

Deep Learning for Unmanned Aerial Vehicle-Based Object Detection and Tracking: A survey

Xin Wu
, Wei Li
, Danfeng Hong
, Ran Tao
, Qian Du

School of Information and Electronics

Beijing Institute of Technology
German Aerospace Center
Mississippi State University

Research output: Contribution to journal › Article › peer-review

321 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	91-124
Number of pages	34
Journal	IEEE Geoscience and Remote Sensing Magazine
Volume	10
Issue number	1
DOIs	https://doi.org/10.1109/MGRS.2021.3115137
Publication status	Published - 1 Mar 2022

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10.1109/MGRS.2021.3115137

Cite this

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abstract = "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.",

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Deep Learning for Unmanned Aerial Vehicle-Based Object Detection and Tracking: A survey

Abstract

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