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Drones
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Advances in Civil Applications of Unmanned Aircraft Systems
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Advances in Civil Applications of Unmanned Aircraft Systems

A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 78745

Special Issue Editors

Dr. Higinio González Jorge

E-Mail Website
Guest Editor
Engineering Physics Group. School of Aerospace Engineering, University of Vigo, Campus Ourense, 32004 Ourense, Spain
Interests: infrastructure maintenance; NDT; UAV; geospatial technology
Special Issues, Collections and Topics in MDPI journals
Dr. Luis Miguel González de Santos

E-Mail Website
Guest Editor
Engineering Physics Group. School of Aerospace Engineering, University of Vigo, Campus Ourense, 32004 Ourense, Spain
Interests: UAVs; path-planning; UAV navigation; NDT; contact inspections; LiDAR; point cloud processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last few years, the use of unmanned aircraft systems (UASs), also known as drones, has rapidly increased. The cost reduction of these systems, coupled with their ability to reach difficult to access areas make them the perfect tools for performing different civil applications. In addition, several payloads for different purposes have been developed in recent years, thus introducing these systems into many new fields.

Motivated by this rapid development, we are excited to invite you to submit a research paper to the Special Issue of Drones titled “Advances in Civil Applications of Unmanned Aircraft Systems”. The aim of this Special Issue is to showcase new innovations presented in unmanned aircraft systems in civil applications, demonstrating their impact in areas such as surveying, infrastructure inspections, mining, precision agriculture, forest management, surveillance, and cargo/logistics. Their applications include drones in different configurations, i.e., fixed-wing, rotary wing, or vertical take-off and landing. Original submissions aligned with the above-mentioned research areas are highly welcomed.

Dr. Higinio González Jorge
Dr. Luis Miguel González-deSantos
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 submissions that pass pre-check are 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. Drones is an international peer-reviewed open access monthly 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 2600 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.

Keywords

  • drone
  • unmanned aircraft system
  • surveying
  • infrastructure
  • mining
  • agriculture
  • forest
  • surveillance
  • cargo
  • logistics
  • photogrammetry
  • remote sensing
  • spraying

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Further information on MDPI's Special Issue polices can be found here.

Related Special Issue

Published Papers (11 papers)

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Research

Jump to: Review, Other

10 pages, 1385 KiB  
Article
Energy-Efficient Inference on the Edge Exploiting TinyML Capabilities for UAVs
by Wamiq Raza, Anas Osman, Francesco Ferrini and Francesco De Natale
Drones 2021, 5(4), 127; https://doi.org/10.3390/drones5040127 - 29 Oct 2021
Cited by 29 | Viewed by 6323
Abstract
In recent years, the proliferation of unmanned aerial vehicles (UAVs) has increased dramatically. UAVs can accomplish complex or dangerous tasks in a reliable and cost-effective way but are still limited by power consumption problems, which pose serious constraints on the flight duration and [...] Read more.
In recent years, the proliferation of unmanned aerial vehicles (UAVs) has increased dramatically. UAVs can accomplish complex or dangerous tasks in a reliable and cost-effective way but are still limited by power consumption problems, which pose serious constraints on the flight duration and completion of energy-demanding tasks. The possibility of providing UAVs with advanced decision-making capabilities in an energy-effective way would be extremely beneficial. In this paper, we propose a practical solution to this problem that exploits deep learning on the edge. The developed system integrates an OpenMV microcontroller into a DJI Tello Micro Aerial Vehicle (MAV). The microcontroller hosts a set of machine learning-enabled inference tools that cooperate to control the navigation of the drone and complete a given mission objective. The goal of this approach is to leverage the new opportunistic features of TinyML through OpenMV including offline inference, low latency, energy efficiency, and data security. The approach is successfully validated on a practical application consisting of the onboard detection of people wearing protection masks in a crowded environment. Full article
(This article belongs to the Special Issue Advances in Civil Applications of Unmanned Aircraft Systems)
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17 pages, 4465 KiB  
Article
Unmanned Aerial Vehicles for Operational Monitoring of Landfills
by Timofey Filkin, Natalia Sliusar, Marco Ritzkowski and Marion Huber-Humer
Drones 2021, 5(4), 125; https://doi.org/10.3390/drones5040125 - 26 Oct 2021
Cited by 18 | Viewed by 6475
Abstract
This study justifies the prospect of using aerial imagery from unmanned aerial vehicles (UAVs) for technological monitoring and operational control of municipal solid waste landfills. It presents the results of surveys (aerial imagery) of a number of Russian landfills, which were carried out [...] Read more.
This study justifies the prospect of using aerial imagery from unmanned aerial vehicles (UAVs) for technological monitoring and operational control of municipal solid waste landfills. It presents the results of surveys (aerial imagery) of a number of Russian landfills, which were carried out using low-cost drones equipped with standard RGB cameras. In the processing of aerial photographs, both photogrammetric data processing algorithms (for constructing orthophotoplans of objects and 3D modeling) and procedures for thematic interpretation of photo images were used. Thematic interpretation was carried out based on lists of requirements for the operating landfills (the lists were compiled on the basis of current legislative acts). Thus, this article proposes framework guidelines for the complex technological monitoring of landfills using relatively simple means of remote control. It shows that compliance with most of the basic requirements for landfill operations, which are listed in both Russian and foreign regulation, can be controlled by unmanned aerial imagery. Thus, all of the main technological operations involving waste at landfills (placement, compaction, intermediate isolation) are able to be controlled remotely; as well as compliance with most of the design and planning requirements associated with the presence and serviceability of certain engineering systems and structures (collection systems for leachate and surface wastewater, etc.); and the state of the landfill body. Cases where the compliance with operating standards cannot be monitored remotely are also considered. It discusses the advantages of air imagery in comparison with space imagery (detail of images, operational efficiency), as well as in comparison with ground inspections (speed, personnel safety). It is shown that in many cases, interpreting the obtained aerial photographs for technological monitoring tasks does not require special image processing and can be performed visually. Based on the analysis of the available world experience, as well as the results of the study, it was concluded that unmanned aerial imagery has great potential for solving problems of waste landfill management. Full article
(This article belongs to the Special Issue Advances in Civil Applications of Unmanned Aircraft Systems)
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17 pages, 9361 KiB  
Article
Application of Fixed-Wing UAV-Based Photogrammetry Data for Snow Depth Mapping in Alpine Conditions
by Matej Masný, Karol Weis and Marek Biskupič
Drones 2021, 5(4), 114; https://doi.org/10.3390/drones5040114 - 11 Oct 2021
Cited by 8 | Viewed by 3353
Abstract
UAV-based photogrammetry has many applications today. Measuring of snow depth using Structure-from-Motion (SfM) techniques is one of them. Determining the depth of snow is very important for a wide range of scientific research activities. In the alpine environment, this information is crucial, especially [...] Read more.
UAV-based photogrammetry has many applications today. Measuring of snow depth using Structure-from-Motion (SfM) techniques is one of them. Determining the depth of snow is very important for a wide range of scientific research activities. In the alpine environment, this information is crucial, especially in the sphere of risk management (snow avalanches). The main aim of this study is to test the applicability of fixed-wing UAV with RTK technology in real alpine conditions to determine snow depth. The territory in West Tatras as a part of Tatra Mountains (Western Carpathians) in the northern part of Slovakia was analyzed. The study area covers more than 1.2 km2 with an elevation of almost 900 m and it is characterized by frequent occurrence of snow avalanches. It was found that the use of different filtering modes (at the level point cloud generation) had no distinct (statistically significant) effect on the result. On the other hand, the significant influence of vegetation characteristics was confirmed. Determination of snow depth based on seasonal digital surface model subtraction can be affected by the process of vegetation compression. The results also point on the importance of RTK methods when mapping areas where it is not possible to place ground control points. Full article
(This article belongs to the Special Issue Advances in Civil Applications of Unmanned Aircraft Systems)
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23 pages, 18832 KiB  
Article
A Visual Aquaculture System Using a Cloud-Based Autonomous Drones
by Naomi A. Ubina, Shyi-Chyi Cheng, Hung-Yuan Chen, Chin-Chun Chang and Hsun-Yu Lan
Drones 2021, 5(4), 109; https://doi.org/10.3390/drones5040109 - 2 Oct 2021
Cited by 19 | Viewed by 8405
Abstract
This paper presents a low-cost and cloud-based autonomous drone system to survey and monitor aquaculture sites. We incorporated artificial intelligence (AI) services using computer vision and combined various deep learning recognition models to achieve scalability and added functionality, in order to perform aquaculture [...] Read more.
This paper presents a low-cost and cloud-based autonomous drone system to survey and monitor aquaculture sites. We incorporated artificial intelligence (AI) services using computer vision and combined various deep learning recognition models to achieve scalability and added functionality, in order to perform aquaculture surveillance tasks. The recognition model is embedded in the aquaculture cloud, to analyze images and videos captured by the autonomous drone. The recognition models detect people, cages, and ship vessels at the aquaculture site. The inclusion of AI functions for face recognition, fish counting, fish length estimation and fish feeding intensity provides intelligent decision making. For the fish feeding intensity assessment, the large amount of data in the aquaculture cloud can be an input for analysis using the AI feeding system to optimize farmer production and income. The autonomous drone and aquaculture cloud services are cost-effective and an alternative to expensive surveillance systems and multiple fixed-camera installations. The aquaculture cloud enables the drone to execute its surveillance task more efficiently with an increased navigation time. The mobile drone navigation app is capable of sending surveillance alerts and reports to users. Our multifeatured surveillance system, with the integration of deep-learning models, yielded high-accuracy results. Full article
(This article belongs to the Special Issue Advances in Civil Applications of Unmanned Aircraft Systems)
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15 pages, 1935 KiB  
Article
In Situ MIMO-WPT Recharging of UAVs Using Intelligent Flying Energy Sources
by Sayed Amir Hoseini, Jahan Hassan, Ayub Bokani and Salil S. Kanhere
Drones 2021, 5(3), 89; https://doi.org/10.3390/drones5030089 - 5 Sep 2021
Cited by 12 | Viewed by 4083
Abstract
Unmanned Aerial Vehicles (UAVs), used in civilian applications such as emergency medical deliveries, precision agriculture, wireless communication provisioning, etc., face the challenge of limited flight time due to their reliance on the on-board battery. Therefore, developing efficient mechanisms for in situ power transfer [...] Read more.
Unmanned Aerial Vehicles (UAVs), used in civilian applications such as emergency medical deliveries, precision agriculture, wireless communication provisioning, etc., face the challenge of limited flight time due to their reliance on the on-board battery. Therefore, developing efficient mechanisms for in situ power transfer to recharge UAV batteries holds potential to extend their mission time. In this paper, we study the use of the far-field wireless power transfer (WPT) technique from specialized, transmitter UAVs (tUAVs) carrying Multiple Input Multiple Output (MIMO) antennas for transferring wireless power to receiver UAVs (rUAVs) in a mission. The tUAVs can fly and adjust their distance to the rUAVs to maximize energy transfer gain. The use of MIMO antennas further boosts the energy reception by narrowing the energy beam toward the rUAVs. The complexity of their dynamic operating environment increases with the growing number of tUAVs and rUAVs with varying levels of energy consumption and residual power. We propose an intelligent trajectory selection algorithm for the tUAVs based on a deep reinforcement learning model called Proximal Policy Optimization (PPO) to optimize the energy transfer gain. The simulation results demonstrate that the PPO-based system achieves about a tenfold increase in flight time for a set of realistic transmit power, distance, sub-band number and antenna numbers. Further, PPO outperforms the benchmark movement strategies of “Traveling Salesman Problem” and “Low Battery First” when used by the tUAVs. Full article
(This article belongs to the Special Issue Advances in Civil Applications of Unmanned Aircraft Systems)
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20 pages, 3239 KiB  
Article
Area-Wide Prediction of Vertebrate and Invertebrate Hole Density and Depth across a Climate Gradient in Chile Based on UAV and Machine Learning
by Paulina Grigusova, Annegret Larsen, Sebastian Achilles, Alexander Klug, Robin Fischer, Diana Kraus, Kirstin Übernickel, Leandro Paulino, Patricio Pliscoff, Roland Brandl, Nina Farwig and Jörg Bendix
Drones 2021, 5(3), 86; https://doi.org/10.3390/drones5030086 - 30 Aug 2021
Cited by 5 | Viewed by 3655
Abstract
Burrowing animals are important ecosystem engineers affecting soil properties, as their burrowing activity leads to the redistribution of nutrients and soil carbon sequestration. The magnitude of these effects depends on the spatial density and depth of such burrows, but a method to derive [...] Read more.
Burrowing animals are important ecosystem engineers affecting soil properties, as their burrowing activity leads to the redistribution of nutrients and soil carbon sequestration. The magnitude of these effects depends on the spatial density and depth of such burrows, but a method to derive this type of spatially explicit data is still lacking. In this study, we test the potential of using consumer-oriented UAV RGB imagery to determine the density and depth of holes created by burrowing animals at four study sites along a climate gradient in Chile, by combining UAV data with empirical field plot observations and machine learning techniques. To enhance the limited spectral information in RGB imagery, we derived spatial layers representing vegetation type and height and used landscape textures and diversity to predict hole parameters. Across-site models for hole density generally performed better than those for depth, where the best-performing model was for the invertebrate hole density (R2 = 0.62). The best models at individual study sites were obtained for hole density in the arid climate zone (R2 = 0.75 and 0.68 for invertebrates and vertebrates, respectively). Hole depth models only showed good to fair performance. Regarding predictor importance, the models heavily relied on vegetation height, texture metrics, and diversity indices. Full article
(This article belongs to the Special Issue Advances in Civil Applications of Unmanned Aircraft Systems)
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17 pages, 7172 KiB  
Article
Moving People Tracking and False Track Removing with Infrared Thermal Imaging by a Multirotor
by Seokwon Yeom
Drones 2021, 5(3), 65; https://doi.org/10.3390/drones5030065 - 20 Jul 2021
Cited by 13 | Viewed by 4454
Abstract
Infrared (IR) thermal imaging can detect the warm temperature of the human body regardless of the light conditions, thus small drones equipped with the IR thermal camera can be utilized to recognize human activity for smart surveillance, road safety, and search and rescue [...] Read more.
Infrared (IR) thermal imaging can detect the warm temperature of the human body regardless of the light conditions, thus small drones equipped with the IR thermal camera can be utilized to recognize human activity for smart surveillance, road safety, and search and rescue missions. However, the unpredictable motion of the drone poses more challenges than a fixed camera. This paper addresses the detection and tracking of people through IR thermal video captured by a multirotor. For object detection, each frame is first registered with a reference frame to compensate for its coordinates. Then, the objects in each frame are segmented through k-means clustering and morphological operations. Falsely detected objects are removed considering the actual size and the shape of the object. The centroid of the segmented area is considered the measured position for target tracking. The track is initialized with two-point differencing initialization, and the target states are continuously estimated by the interacting multiple model (IMM) filter. The nearest neighbor association rule assigns the measurement to the track. Tracks that move slower than the minimum speed are terminated at the proposed criteria. In the experiments, three videos were captured with a long-wave IR band thermal imaging camera mounted on a multirotor. In the first and second videos, eight pedestrians on a pavement and three hikers on a mountain on winter nights were captured, respectively. In the third video, two walking people with complex backgrounds were captured on a windy summer day. The image characteristics vary between videos depending on the climate and surrounding objects, but the proposed scheme shows the robust performance in all cases; the average root mean squared errors in position and velocity are obtained as 0.08 m and 0.53 m/s, respectively for the first video, 0.06 m and 0.58 m/s, respectively for the second video, and 0.18 m and 1.84 m/s, respectively for the third video. The proposed method reduces false tracks from 10 to 1 in the third video. Full article
(This article belongs to the Special Issue Advances in Civil Applications of Unmanned Aircraft Systems)
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12 pages, 21432 KiB  
Article
Development of UAV-Based PM2.5 Monitoring System
by Huda Jamal Jumaah, Bahareh Kalantar, Alfian Abdul Halin, Shattri Mansor, Naonori Ueda and Sarah Jamal Jumaah
Drones 2021, 5(3), 60; https://doi.org/10.3390/drones5030060 - 13 Jul 2021
Cited by 39 | Viewed by 6748
Abstract
This paper proposes a UAV-based PM2.5 air quality and temperature-humidity monitoring system. The system includes an air quality detector comprising four Arduino sensor modules. Specifically, it includes a dust (DSM501A) sensor and a temperature and humidity (DHT11) sensor. The NEO-6M GPS module [...] Read more.
This paper proposes a UAV-based PM2.5 air quality and temperature-humidity monitoring system. The system includes an air quality detector comprising four Arduino sensor modules. Specifically, it includes a dust (DSM501A) sensor and a temperature and humidity (DHT11) sensor. The NEO-6M GPS module and DS3231 real-time module are also included for input visualization. A DIY SD card logging shield and memory module is also available for data recording purposes. The Arduino-based board houses multiple sensors and all are programmable using the Arduino integrated development environment (IDE) coding tool. Measurements conducted in a vertical flight path show promise where comparisons with ground truth references data showed good similarity. Overall, the results point to the idea that a light-weight and portable system can be used for accurate and reliable remote sensing data collection (in this case, PM2.5 concentration data and environmental data). Full article
(This article belongs to the Special Issue Advances in Civil Applications of Unmanned Aircraft Systems)
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15 pages, 15831 KiB  
Article
Visual SLAM for Indoor Livestock and Farming Using a Small Drone with a Monocular Camera: A Feasibility Study
by Sander Krul, Christos Pantos, Mihai Frangulea and João Valente
Drones 2021, 5(2), 41; https://doi.org/10.3390/drones5020041 - 19 May 2021
Cited by 56 | Viewed by 10934
Abstract
Real-time data collection and decision making with drones will play an important role in precision livestock and farming. Drones are already being used in precision agriculture. Nevertheless, this is not the case for indoor livestock and farming environments due to several challenges and [...] Read more.
Real-time data collection and decision making with drones will play an important role in precision livestock and farming. Drones are already being used in precision agriculture. Nevertheless, this is not the case for indoor livestock and farming environments due to several challenges and constraints. These indoor environments are limited in physical space and there is the localization problem, due to GPS unavailability. Therefore, this work aims to give a step toward the usage of drones for indoor farming and livestock management. To investigate on the drone positioning in these workspaces, two visual simultaneous localization and mapping (VSLAM)—LSD-SLAM and ORB-SLAM—algorithms were compared using a monocular camera onboard a small drone. Several experiments were carried out in a greenhouse and a dairy farm barn with the absolute trajectory and the relative pose error being analyzed. It was found that the approach that suits best these workspaces is ORB-SLAM. This algorithm was tested by performing waypoint navigation and generating maps from the clustered areas. It was shown that aerial VSLAM could be achieved within these workspaces and that plant and cattle monitoring could benefit from using affordable and off-the-shelf drone technology. Full article
(This article belongs to the Special Issue Advances in Civil Applications of Unmanned Aircraft Systems)
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Review

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29 pages, 3821 KiB  
Review
Drone-Based Non-Destructive Inspection of Industrial Sites: A Review and Case Studies
by Parham Nooralishahi, Clemente Ibarra-Castanedo, Shakeb Deane, Fernando López, Shashank Pant, Marc Genest, Nicolas P. Avdelidis and Xavier P. V. Maldague
Drones 2021, 5(4), 106; https://doi.org/10.3390/drones5040106 - 29 Sep 2021
Cited by 74 | Viewed by 16126
Abstract
Using aerial platforms for Non-Destructive Inspection (NDI) of large and complex structures is a growing field of interest in various industries. Infrastructures such as: buildings, bridges, oil and gas, etc. refineries require regular and extensive inspections. The inspection reports are used to plan [...] Read more.
Using aerial platforms for Non-Destructive Inspection (NDI) of large and complex structures is a growing field of interest in various industries. Infrastructures such as: buildings, bridges, oil and gas, etc. refineries require regular and extensive inspections. The inspection reports are used to plan and perform required maintenance, ensuring their structural health and the safety of the workers. However, performing these inspections can be challenging due to the size of the facility, the lack of easy access, the health risks for the inspectors, or several other reasons, which has convinced companies to invest more in drones as an alternative solution to overcome these challenges. The autonomous nature of drones can assist companies in reducing inspection time and cost. Moreover, the employment of drones can lower the number of required personnel for inspection and can increase personnel safety. Finally, drones can provide a safe and reliable solution for inspecting hard-to-reach or hazardous areas. Despite the recent developments in drone-based NDI to reliably detect defects, several limitations and challenges still need to be addressed. In this paper, a brief review of the history of unmanned aerial vehicles, along with a comprehensive review of studies focused on UAV-based NDI of industrial and commercial facilities, are provided. Moreover, the benefits of using drones in inspections as an alternative to conventional methods are discussed, along with the challenges and open problems of employing drones in industrial inspections, are explored. Finally, some of our case studies conducted in different industrial fields in the field of Non-Destructive Inspection are presented. Full article
(This article belongs to the Special Issue Advances in Civil Applications of Unmanned Aircraft Systems)
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Other

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14 pages, 4502 KiB  
Technical Note
Design and Implementation of Intelligent Inspection and Alarm Flight System for Epidemic Prevention
by Jiwei Fan, Xiaogang Yang, Ruitao Lu, Xueli Xie and Weipeng Li
Drones 2021, 5(3), 68; https://doi.org/10.3390/drones5030068 - 27 Jul 2021
Cited by 14 | Viewed by 3537
Abstract
Unmanned aerial vehicles (UAV) and related technologies have played an active role in the prevention and control of novel coronaviruses at home and abroad, especially in epidemic prevention, surveillance, and elimination. However, the existing UAVs have a single function, limited processing capacity, and [...] Read more.
Unmanned aerial vehicles (UAV) and related technologies have played an active role in the prevention and control of novel coronaviruses at home and abroad, especially in epidemic prevention, surveillance, and elimination. However, the existing UAVs have a single function, limited processing capacity, and poor interaction. To overcome these shortcomings, we designed an intelligent anti-epidemic patrol detection and warning flight system, which integrates UAV autonomous navigation, deep learning, intelligent voice, and other technologies. Based on the convolution neural network and deep learning technology, the system possesses a crowd density detection method and a face mask detection method, which can detect the position of dense crowds. Intelligent voice alarm technology was used to achieve an intelligent alarm system for abnormal situations, such as crowd-gathering areas and people without masks, and to carry out intelligent dissemination of epidemic prevention policies, which provides a powerful technical means for epidemic prevention and delaying their spread. To verify the superiority and feasibility of the system, high-precision online analysis was carried out for the crowd in the inspection area, and pedestrians’ faces were detected on the ground to identify whether they were wearing a mask. The experimental results show that the mean absolute error (MAE) of the crowd density detection was less than 8.4, and the mean average precision (mAP) of face mask detection was 61.42%. The system can provide convenient and accurate evaluation information for decision-makers and meets the requirements of real-time and accurate detection. Full article
(This article belongs to the Special Issue Advances in Civil Applications of Unmanned Aircraft Systems)
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