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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| Published in | Journal of systems architecture Vol. 117; p. 102152 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.08.2021
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| Subjects | |
| Online Access | Get full text |
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| Abstract | “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. |
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| AbstractList | “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. |
| ArticleNumber | 102152 |
| Author | Srivastava, Srishti Mittal, Sparsh Narayan, Sarthak |
| Author_xml | – sequence: 1 givenname: Srishti orcidid: 0000-0003-3634-7279 surname: Srivastava fullname: Srivastava, Srishti email: srishtisrivastava.ai@gmail.com organization: CSE Department, IIT Dharwad, India – sequence: 2 givenname: Sarthak surname: Narayan fullname: Narayan, Sarthak email: sarthak.narayan.nitt@gmail.com organization: ECE Department, NIT Trichy, India – sequence: 3 givenname: Sparsh orcidid: 0000-0002-2908-993X surname: Mittal fullname: Mittal, Sparsh email: sparshfec@iitr.ac.in organization: ECE Department, IIT Roorkee, India |
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| Keywords | Deep learning “Unmanned aerial vehicle” (UAV) Review Drone Vehicle detection Object detection |
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