Special Issue "Deep Learning Techniques for Manned and Unmanned Ground, Aerial and Marine Vehicles"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 31 October 2021.

Special Issue Editors

Prof. Dr. Ahmad Taher Azar
E-Mail Website1 Website2 Website3
Guest Editor
1. College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
2. Faculty of Computers and Artificial Intelligence, Benha University, Benha 13518, Egypt
Interests: control theory & applications; robotics; process control; artificial intelligence; machine learning
Special Issues and Collections in MDPI journals
Prof. Dr. Anis Koubaa
E-Mail Website1 Website2
Guest Editor
1. Robotics and Internet-of-Things Lab, Prince Sultan University, Riyadh 11586, Saudi Arabia
2. CISTER and ISTER, INESC-TEC, ISEP, Polytechnic Institute of Porto, 4249-015 Porto, Portugal
Interests: aerial image processing; deep learning; precision agriculture; remote sensing; computer vision
Prof. Dr. Alaa Khamis
E-Mail Website
Guest Editor
General Motors Canada, 500 Wentworth St W, Oshawa, ON L1J 6J2, Canada
Interests: smart mobility; autonomous and connected vehicles; cognitive IoT; machine learning; combinatorial opti-mization
Prof. Dr. Ibrahim A. Hameed
E-Mail Website
Guest Editor
Department of ICT and Natural Sciences, Norwegian University of Science and Technology, Larsg ardsveg-en, 26009 Alesund, Norway
Interests: artificial intelligence; field robotics; autonomous navigation; path planning; automation and control
Dr. Gabriella Casalino
E-Mail Website
Guest Editor
Department of Computer Science, University of Bari Aldo Moro, Via Orabona, 4-70125 Bari, Italy
Interests: Computational intelligence; knowledge discovery from data; intelligent data analysis; matrix factorizations
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Manned and unmanned ground, aerial, and marine vehicles enable many promising and revolutionary civilian and military applications that will change our lives in the near future. These applications include, but are not limited to, surveillance, search and rescue, environment monitoring, infrastructure monitoring, self-driving cars, contactless last-mile delivery vehicles, autonomous ships, precision agriculture, and transmission line inspection, to name just a few. These vehicles will benefit from advances in deep learning as a subfield of machine learning able to endow these vehicles with different capabilities such as perception, situation awareness, planning, and intelligent control. Deep learning models also have the ability to generate actionable insights into the complex structures of large data sets.

In recent years, deep learning research has received increasing attention from researchers in academia, government laboratories, and industry. These research activities have borne some fruit in tackling some of the remaining challenging problems of manned and unmanned ground, aerial, and marine vehicles. Moreover, deep learning methods have recently been actively developed in other areas of machine learning, including reinforcement training and transfer/meta-learning, whereas standard deep learning methods such as RNN (recent neural network) and CNN (coevolutionary neural networks). 

The purpose of this Special Issue is to report recent applications of deep learning approaches in manned and unmanned ground, aerial, and marine vehicles. Topics include but are not limited to:

  • Cognitive data collection;
  • Data cleansing;
  • Data compression;
  • Multisensor data fusion;
  • Vehicle localization;
  • Perception systems;
  • AI for automation systems;
  • Object detection, localization, and tracking;
  • Situation awareness;
  • Vehicle control;
  • Autonomous vehicles;
  • Connected vehicles;
  • Self-driving cars;
  • Generative adversarial networks (GANs);
  • Collective intelligence;
  • Multiagent systems;
  • Platooning, flocking, and self-organization;
  • Applications: unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), unmanned underwater vehicles (UUVs), and unmanned surface vehicles (USVs), self-driving cars, delivery robots, search and rescue, reconnaissance, surveillance, swarm robotics, etc.

Prof. Dr. Ahmad Taher Azar
Prof. Dr. Anis Koubaa
Prof. Dr. Alaa Khamis
Prof. Dr. Ibrahim A. Hameed
Dr. Gabriella Casalino
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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Research

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Open AccessArticle
Vehicle Detection from Aerial Images Using Deep Learning: A Comparative Study
Electronics 2021, 10(7), 820; https://doi.org/10.3390/electronics10070820 - 30 Mar 2021
Cited by 1 | Viewed by 461
Abstract
This paper addresses the problem of car detection from aerial images using Convolutional Neural Networks (CNNs). This problem presents additional challenges as compared to car (or any object) detection from ground images because the features of vehicles from aerial images are more difficult [...] Read more.
This paper addresses the problem of car detection from aerial images using Convolutional Neural Networks (CNNs). This problem presents additional challenges as compared to car (or any object) detection from ground images because the features of vehicles from aerial images are more difficult to discern. To investigate this issue, we assess the performance of three state-of-the-art CNN algorithms, namely Faster R-CNN, which is the most popular region-based algorithm, as well as YOLOv3 and YOLOv4, which are known to be the fastest detection algorithms. We analyze two datasets with different characteristics to check the impact of various factors, such as the UAV’s (unmanned aerial vehicle) altitude, camera resolution, and object size. A total of 52 training experiments were conducted to account for the effect of different hyperparameter values. The objective of this work is to conduct the most robust and exhaustive comparison between these three cutting-edge algorithms on the specific domain of aerial images. By using a variety of metrics, we show that the difference between YOLOv4 and YOLOv3 on the two datasets is statistically insignificant in terms of Average Precision (AP) (contrary to what was obtained on the COCO dataset). However, both of them yield markedly better performance than Faster R-CNN in most configurations. The only exception is that both of them exhibit a lower recall when object sizes and scales in the testing dataset differ largely from those in the training dataset. Full article
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Review

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Open AccessReview
Drone Deep Reinforcement Learning: A Review
Electronics 2021, 10(9), 999; https://doi.org/10.3390/electronics10090999 - 22 Apr 2021
Viewed by 308
Abstract
Unmanned Aerial Vehicles (UAVs) are increasingly being used in many challenging and diversified applications. These applications belong to the civilian and the military fields. To name a few; infrastructure inspection, traffic patrolling, remote sensing, mapping, surveillance, rescuing humans and animals, environment monitoring, and [...] Read more.
Unmanned Aerial Vehicles (UAVs) are increasingly being used in many challenging and diversified applications. These applications belong to the civilian and the military fields. To name a few; infrastructure inspection, traffic patrolling, remote sensing, mapping, surveillance, rescuing humans and animals, environment monitoring, and Intelligence, Surveillance, Target Acquisition, and Reconnaissance (ISTAR) operations. However, the use of UAVs in these applications needs a substantial level of autonomy. In other words, UAVs should have the ability to accomplish planned missions in unexpected situations without requiring human intervention. To ensure this level of autonomy, many artificial intelligence algorithms were designed. These algorithms targeted the guidance, navigation, and control (GNC) of UAVs. In this paper, we described the state of the art of one subset of these algorithms: the deep reinforcement learning (DRL) techniques. We made a detailed description of them, and we deduced the current limitations in this area. We noted that most of these DRL methods were designed to ensure stable and smooth UAV navigation by training computer-simulated environments. We realized that further research efforts are needed to address the challenges that restrain their deployment in real-life scenarios. Full article
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