A survey of deep learning techniques for vehicle detection from UAV images

“Unmanned aerial vehicles” (UAVs) are now being used for a wide range of surveillance applications. Specifically, the detection of on-ground vehicles from UAV images has attracted significant attention due to its potential in applications such as traffic management, parking lot management, and facil...

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Bibliographic Details
Published inJournal of systems architecture Vol. 117; p. 102152
Main Authors Srivastava, Srishti, Narayan, Sarthak, Mittal, Sparsh
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.08.2021
Subjects
Deep learning
Drone
Object detection
Review
Vehicle detection
“Unmanned aerial vehicle” (UAV)
Deep learning
“Unmanned aerial vehicle” (UAV)
Review
Drone
Vehicle detection
Object detection
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Summary:“Unmanned aerial vehicles” (UAVs) are now being used for a wide range of surveillance applications. Specifically, the detection of on-ground vehicles from UAV images has attracted significant attention due to its potential in applications such as traffic management, parking lot management, and facilitating rescue operations in disaster zones and rugged terrains. This paper presents a survey of deep learning techniques for performing on-ground vehicle detection from aerial imagery captured using UAVs (also known as drones). We review the works in terms of their approach to improve accuracy and reduce computation overhead and their optimization objective. We show the similarities and differences of various techniques and also highlight the future challenges in this area. This survey will benefit researchers in the area of artificial intelligence, traffic surveillance, and applications of UAVs.
ISSN:1383-7621
1873-6165
DOI:10.1016/j.sysarc.2021.102152