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...

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Veröffentlicht in:IEEE transaction on neural networks and learning systems Jg. 33; H. 11; S. 6047 - 6067
Hauptverfasser: Bouguettaya, Abdelmalek, Zarzour, Hafed, Kechida, Ahmed, Taberkit, Amine Mohammed
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.11.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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
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Zusammenfassung: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.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2021.3080276