Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.1
4.8
Journals
Drones
Special Issues
Advances in UAV Detection, Classification and Tracking-II
share announcement

Advances in UAV Detection, Classification and Tracking-II

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

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 16904

Special Issue Editors

Dr. Zain Anwar Ali

E-Mail Website
Guest Editor
1. School of Physics and Electronic Engineering, JiaYing University, Meizhou, China
2. Department of Electronic Engineering, Sir Syed University of Engineering and Technology, Karachi, Pakistan
Interests: autonomous unmanned system (i.e. aerial and underwater vehicles)
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Quanmin Zhu

E-Mail Website
Guest Editor
School of Engineering, University of the West of England, Frenchay Campus, Coldharbour Lane, Bristol BS16 1QY, UK
Interests: complex dynamic system identification and control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, the research and development of unmanned aerial vehicles (UAV) and their state-of-the-art applications have become one of the fastest-growing fields among scholars. There have been advancements in core areas of UAV technology, such as detection, including visible-band and thermal infrared imaging, radio frequency, and radar; classifications, such as micro, mini, close range, short range, medium range, medium range endurance, low-altitude deep penetration, low-altitude long endurance, and medium-altitude long endurance; tracking, including lateral tracking, vertical tracking, moving aerial pan with moving target, and moving aerial tilt with moving target.

This Special Issue intends to propose solutions to problems associated with UAV detection, UAV classifications, and UAV tracking.

Dr. Zain Anwar Ali
Prof. Dr. Quanmin Zhu
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

  • aerial robots
  • unmanned aerial vehicles
  • path planning
  • aerial formation
  • drones
  • trajectory tracking
  • aerial manipulation
  • swarm intelligence in robotics
  • bio-inspired computations for UAVs

Published Papers (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

16 pages, 1558 KiB  
Article
On-Board Small-Scale Object Detection for Unmanned Aerial Vehicles (UAVs)
by Zubair Saeed, Muhammad Haroon Yousaf, Rehan Ahmed, Sergio A. Velastin and Serestina Viriri
Drones 2023, 7(5), 310; https://doi.org/10.3390/drones7050310 - 06 May 2023
Cited by 6 | Viewed by 2730
Abstract
Object detection is a critical task that becomes difficult when dealing with onboard detection using aerial images and computer vision technique. The main challenges with aerial images are small target sizes, low resolution, occlusion, attitude, and scale variations, which affect the performance of [...] Read more.
Object detection is a critical task that becomes difficult when dealing with onboard detection using aerial images and computer vision technique. The main challenges with aerial images are small target sizes, low resolution, occlusion, attitude, and scale variations, which affect the performance of many object detectors. The accuracy of the detection and the efficiency of the inference are always trade-offs. We modified the architecture of CenterNet and used different CNN-based backbones of ResNet18, ResNet34, ResNet50, ResNet101, ResNet152, Res2Net50, Res2Net101, DLA-34, and hourglass14. A comparison of the modified CenterNet with nine CNN-based backbones is conducted and validated using three challenging datasets, i.e., VisDrone, Stanford Drone dataset (SSD), and AU-AIR. We also implemented well-known off-the-shelf object detectors, i.e., YoloV1 to YoloV7, SSD-MobileNet-V2, and Faster RCNN. The proposed approach and state-of-the-art object detectors are optimized and then implemented on cross-edge platforms, i.e., NVIDIA Jetson Xavier, NVIDIA Jetson Nano, and Neuro Compute Stick 2 (NCS2). A detailed comparison of performance between edge platforms is provided. Our modified CenterNet combination with hourglass as a backbone achieved 91.62%, 75.61%, and 34.82% mAP using the validation sets of AU-AIR, SSD, and VisDrone datasets, respectively. An FPS of 40.02 was achieved using the ResNet18 backbone. We also compared our approach with the latest cutting-edge research and found promising results for both discrete GPU and edge platforms. Full article
(This article belongs to the Special Issue Advances in UAV Detection, Classification and Tracking-II)
►▼ Show Figures

Figure 1

22 pages, 7695 KiB  
Article
STFTrack: Spatio-Temporal-Focused Siamese Network for Infrared UAV Tracking
by Xueli Xie, Jianxiang Xi, Xiaogang Yang, Ruitao Lu and Wenxin Xia
Drones 2023, 7(5), 296; https://doi.org/10.3390/drones7050296 - 28 Apr 2023
Cited by 1 | Viewed by 1308
Abstract
The rapid popularity of UAVs has encouraged the development of Anti-UAV technology. Infrared-detector-based visual tracking for UAVs provides an encouraging solution for Anti-UAVs. However, it still faces the problem of tracking instability caused by environmental thermal crossover and similar distractors. To address these [...] Read more.
The rapid popularity of UAVs has encouraged the development of Anti-UAV technology. Infrared-detector-based visual tracking for UAVs provides an encouraging solution for Anti-UAVs. However, it still faces the problem of tracking instability caused by environmental thermal crossover and similar distractors. To address these issues, we propose a spatio-temporal-focused Siamese network for infrared UAV tracking, called STFTrack. This method employs a two-level target focusing strategy from global to local. First, a feature pyramid-based Siamese backbone is constructed to enhance the feature expression of infrared UAVs through cross-scale feature fusion. By combining template and motion features, we guide prior anchor boxes towards the suspicious region to enable adaptive search region selection, thus effectively suppressing background interference and generating high-quality candidates. Furthermore, we propose an instance-discriminative RCNN based on metric learning to focus on the target UAV among candidates. By measuring calculating the feature distance between the candidates and the template, it assists in discriminating the optimal target from the candidates, thus improving the discrimination of the proposed method to infrared UAV. Extensive experiments on the Anti-UAV dataset demonstrate that the proposed method achieves outstanding performance for infrared tracking, with 91.2% precision, 66.6% success rate, and 67.7% average overlap accuracy, and it exceeded the baseline algorithm by 2.3%, 2.7%, and 3.5%, respectively. The attribute-based evaluation demonstrates that the proposed method achieves robust tracking effects on challenging scenes such as fast motion, thermal crossover, and similar distractors. Evaluation on the LSOTB-TIR dataset shows that the proposed method reaches a precision of 77.2% and a success rate of 63.4%, outperforming other advanced trackers. Full article
(This article belongs to the Special Issue Advances in UAV Detection, Classification and Tracking-II)
►▼ Show Figures

Figure 1

15 pages, 5291 KiB  
Article
Exploring Radar Micro-Doppler Signatures for Recognition of Drone Types
by Jun Yan, Huiping Hu, Jiangkun Gong, Deyong Kong and Deren Li
Drones 2023, 7(4), 280; https://doi.org/10.3390/drones7040280 - 21 Apr 2023
Cited by 6 | Viewed by 2476
Abstract
In this study, we examine the use of micro-Doppler signals produced by different blades (i.e., puller and lifting blades) to aid in radar-based target recognition of small drones. We categorize small drones into three types based on their blade types: fixed-wing drones with [...] Read more.
In this study, we examine the use of micro-Doppler signals produced by different blades (i.e., puller and lifting blades) to aid in radar-based target recognition of small drones. We categorize small drones into three types based on their blade types: fixed-wing drones with only puller blades, multi-rotor drones with only lifting blades, and hybrid vertical take-off and landing (VTOL) fixed-wing drones with both lifting and puller blades. We quantify the radar signatures of the three drones using statistical measures, such as signal-to-noise ratio (SNR), signal-to-clutter ratio (SCR), Doppler speed, Doppler frequency difference (DFD), and Doppler magnitude ratio (DMR). Our findings show that the micro-Doppler signals of lifting blades in all three drone types were stronger than those of puller blades. Specifically, the DFD and DMR values of pusher blades were below 100 Hz and 0.3, respectively, which were much smaller than the 200 Hz and 0.8 values for lifting blades. The micro-Doppler signals of the puller blades were weaker and more stable than those of the lifting blades. Our study demonstrates the potential of using micro-Doppler signatures modulated by different blades for improving drone detection and the identification of drone types by drone detection radar. Full article
(This article belongs to the Special Issue Advances in UAV Detection, Classification and Tracking-II)
►▼ Show Figures

Figure 1

20 pages, 9605 KiB  
Article
Application of Image Processing Techniques for UAV Detection Using Deep Learning and Distance-Wise Analysis
by Vedanshu Dewangan, Aditya Saxena, Rahul Thakur and Shrivishal Tripathi
Drones 2023, 7(3), 174; https://doi.org/10.3390/drones7030174 - 03 Mar 2023
Cited by 7 | Viewed by 5209
Abstract
Drones have many applications in our daily lives and can be employed for agricultural, military, commercial, disaster relief, research and development, and many other purposes. There has been a significant increase in the usage of small drones/unmanned aerial vehicles in recent years. Consequently, [...] Read more.
Drones have many applications in our daily lives and can be employed for agricultural, military, commercial, disaster relief, research and development, and many other purposes. There has been a significant increase in the usage of small drones/unmanned aerial vehicles in recent years. Consequently, there is a rising potential for small drones to be misused for illegal activities, such as terrorism and drug smuggling. Hence, there is a need for accurate and reliable UAV identification that can be used in various environments. In this paper, different versions of the current state-of-the-art object detection model, i.e., YOLO models, are used, by working on the principles of computer vision and deep learning to detect small UAVs. To improve the accuracy of small UAV detection, this paper proposes the application of various image-processing techniques to the current detection model, which has resulted in a significant performance increase. In this study, a mAP score of 96.7% was obtained for an IoU threshold of 50% along with a precision value of 95% and a recall of 95.6%. Distance-wise analysis of drones (i.e., for close, mid, and far ranges) was also performed to measure distance-wise accuracies. Full article
(This article belongs to the Special Issue Advances in UAV Detection, Classification and Tracking-II)
►▼ Show Figures

Figure 1

24 pages, 4656 KiB  
Article
A Motion-Aware Siamese Framework for Unmanned Aerial Vehicle Tracking
by Lifan Sun, Jinjin Zhang, Zhe Yang and Bo Fan
Drones 2023, 7(3), 153; https://doi.org/10.3390/drones7030153 - 22 Feb 2023
Cited by 2 | Viewed by 1307
Abstract
In recent years, visual tracking has been employed in all walks of life. The Siamese trackers formulate the tracking problem as a template-matching process, and most of them can meet the real-time requirements, making them more suitable for UAV tracking. Because existing trackers [...] Read more.
In recent years, visual tracking has been employed in all walks of life. The Siamese trackers formulate the tracking problem as a template-matching process, and most of them can meet the real-time requirements, making them more suitable for UAV tracking. Because existing trackers can only use the first frame of a video sequence as a reference, the appearance of the tracked target will change when an occlusion, fast motion, or similar target appears, resulting in tracking drift. It is difficult to recover the tracking process once the drift phenomenon occurs. Therefore, we propose a motion-aware Siamese framework to assist Siamese trackers in detecting tracking drift over time. The base tracker first outputs the original tracking results, after which the drift detection module determines whether or not tracking drift occurs. Finally, the corresponding tracking recovery strategies are implemented. More stable and reliable tracking results can be obtained using the Kalman filter’s short-term prediction ability and more effective tracking recovery strategies to avoid tracking drift. We use the Siamese region proposal network (SiamRPN), a typical representative of an anchor-based algorithm, and Siamese classification and regression (SiamCAR), a typical representative of an anchor-free algorithm, as the base trackers to test the effectiveness of the proposed method. Experiments were carried out on three public datasets: UAV123, UAV20L, and UAVDT. The modified trackers (MaSiamRPN and MaSiamCAR) both outperformed the base tracker. Full article
(This article belongs to the Special Issue Advances in UAV Detection, Classification and Tracking-II)
►▼ Show Figures

Figure 1

Review

Jump to: Research

32 pages, 1783 KiB  
Review
A Comprehensive Review of Scab Disease Detection on Rosaceae Family Fruits via UAV Imagery
by Zain Anwar Ali, Chenguang Yang, Amber Israr and Quanmin Zhu
Drones 2023, 7(2), 97; https://doi.org/10.3390/drones7020097 - 30 Jan 2023
Cited by 4 | Viewed by 2560
Abstract
Disease detection in plants is essential for food security and economic stability. Unmanned aerial vehicle (UAV) imagery and artificial intelligence (AI) are valuable tools for it. The purpose of this review is to gather several methods used by our peers recently, hoping to [...] Read more.
Disease detection in plants is essential for food security and economic stability. Unmanned aerial vehicle (UAV) imagery and artificial intelligence (AI) are valuable tools for it. The purpose of this review is to gather several methods used by our peers recently, hoping to provide some knowledge and assistance for researchers and farmers so that they can employ these technologies more advantageously. The studies reviewed in this paper focused on Scab detection in Rosaceae family fruits. Feature extraction, segmentation, and classification methods for processing the UAV-obtained images and detecting the diseases are discussed briefly. The advantages and limitations of diverse kinds of UAVs and imaging sensors are also explained. The widely applied methods for image analysis are machine learning (ML)-based models, and the extensively used UAV platforms are rotary-wing UAVs. Recent technologies that cope with challenges related to disease detection using UAV imagery are also detailed in this paper. Some challenging issues such as higher costs, limited batteries and flying time, huge and complex data, low resolution, and noisy images, etc., still require future consideration. The prime significance of this paper is to promote automation and user-friendly technologies in Scab detection. Full article
(This article belongs to the Special Issue Advances in UAV Detection, Classification and Tracking-II)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop