Vehicle Detection From UAV Imagery With Deep Learning: A Review

Vehicle detection from unmanned aerial vehicle (UAV) imagery is one of the most important tasks in a large number of computer vision-based applications. This crucial task needed to be done with high accuracy and speed. However, it is a very challenging task due to many characteristics related to the...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE transaction on neural networks and learning systems Ročník 33; číslo 11; s. 6047 - 6067
Hlavní autoři:	Bouguettaya, Abdelmalek, Zarzour, Hafed, Kechida, Ahmed, Taberkit, Amine Mohammed
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.11.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Artificial neural networks Autoencoders Computer architecture Computer vision convolutional neural networks (CNNs) Datasets Deep learning Generative adversarial networks generative adversarial networks (GANs) Machine learning Neural networks Object detection Recurrent neural networks recurrent neural networks (RNNs) Task analysis unmanned aerial vehicle (UAV) Unmanned aerial vehicles Vehicle detection
ISSN:	2162-237X, 2162-2388, 2162-2388
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	Vehicle detection from unmanned aerial vehicle (UAV) imagery is one of the most important tasks in a large number of computer vision-based applications. This crucial task needed to be done with high accuracy and speed. However, it is a very challenging task due to many characteristics related to the aerial images and the used hardware, such as different vehicle sizes, orientations, types, density, limited datasets, and inference speed. In recent years, many classical and deep-learning-based methods have been proposed in the literature to address these problems. Handed engineering- and shallow learning-based techniques suffer from poor accuracy and generalization to other complex cases. Deep-learning-based vehicle detection algorithms achieved better results due to their powerful learning ability. In this article, we provide a review on vehicle detection from UAV imagery using deep learning techniques. We start by presenting the different types of deep learning architectures, such as convolutional neural networks, recurrent neural networks, autoencoders, generative adversarial networks, and their contribution to improve the vehicle detection task. Then, we focus on investigating the different vehicle detection methods, datasets, and the encountered challenges all along with the suggested solutions. Finally, we summarize and compare the techniques used to improve vehicle detection from UAV-based images, which could be a useful aid to researchers and developers to select the most adequate method for their needs.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	2162-237X 2162-2388 2162-2388
DOI:	10.1109/TNNLS.2021.3080276