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17 pages, 7930 KiB

Open AccessArticle

DTPPO: Dual-Transformer Encoder-Based Proximal Policy Optimization for Multi-UAV Navigation in Unseen Complex Environments

by Anning Wei, Jintao Liang, Kaiyuan Lin, Ziyue Li and Rui Zhao

Drones 2024, 8(12), 720; https://doi.org/10.3390/drones8120720 - 29 Nov 2024

Viewed by 568

Abstract

Existing multi-agent deep reinforcement learning (MADRL) methods for multi-UAV navigation face challenges in generalization, particularly when applied to unseen complex environments. To address these limitations, we propose a Dual-Transformer Encoder-Based Proximal Policy Optimization (DTPPO) method. DTPPO enhances multi-UAV collaboration through a [...] Read more.

Existing multi-agent deep reinforcement learning (MADRL) methods for multi-UAV navigation face challenges in generalization, particularly when applied to unseen complex environments. To address these limitations, we propose a Dual-Transformer Encoder-Based Proximal Policy Optimization (DTPPO) method. DTPPO enhances multi-UAV collaboration through a Spatial Transformer, which models inter-agent dynamics, and a Temporal Transformer, which captures temporal dependencies to improve generalization across diverse environments. This architecture allows UAVs to navigate new, unseen environments without retraining. Extensive simulations demonstrate that DTPPO outperforms current MADRL methods in terms of transferability, obstacle avoidance, and navigation efficiency across environments with varying obstacle densities. The results confirm DTPPO’s effectiveness as a robust solution for multi-UAV navigation in both known and unseen scenarios. Full article

(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV)

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20 pages, 4297 KiB

Open AccessArticle

Precision and Efficiency in Dam Crack Inspection: A Lightweight Object Detection Method Based on Joint Distillation for Unmanned Aerial Vehicles (UAVs)

by Hangcheng Dong, Nan Wang, Dongge Fu, Fupeng Wei, Guodong Liu and Bingguo Liu

Drones 2024, 8(11), 692; https://doi.org/10.3390/drones8110692 - 19 Nov 2024

Viewed by 738

Abstract

Dams in their natural environment will gradually develop cracks and other forms of damage. If not detected and repaired in time, the structural strength of the dam may be reduced, and it may even collapse. Repairing cracks and defects in dams is very [...] Read more.

Dams in their natural environment will gradually develop cracks and other forms of damage. If not detected and repaired in time, the structural strength of the dam may be reduced, and it may even collapse. Repairing cracks and defects in dams is very important to ensure their normal operation. Traditional detection methods rely on manual inspection, which consumes a lot of time and labor, while deep learning methods can greatly alleviate this problem. However, previous studies have often focused on how to better detect crack defects, with the corresponding image resolution not being particularly high. In this study, targeting the scenario of real-time detection by drones, we propose an automatic detection method for dam crack targets directly on high-resolution remote sensing images. First, for high-resolution remote sensing images, we designed a sliding window processing method and proposed corresponding methods to eliminate redundant detection frames. Then, we introduced a Gaussian distribution in the loss function to calculate the similarity of predicted frames and incorporated a self-attention mechanism in the spatial pooling module to further enhance the detection performance of crack targets at various scales. Finally, we proposed a pruning-after-distillation scheme, using the compressed model as the student and the pre-compression model as the teacher and proposed a joint distillation method that allows more efficient distillation under this compression relationship between teacher and student models. Ultimately, a high-performance target detection model can be deployed in a more lightweight form for field operations such as UAV patrols. Experimental results show that our method achieves an mAP of 80.4%, with a parameter count of only 0.725 M, providing strong support for future tasks such as UAV field inspections. Full article

(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV)

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24 pages, 5038 KiB

Open AccessArticle

UAV Anomaly Detection Method Based on Convolutional Autoencoder and Support Vector Data Description with 0/1 Soft-Margin Loss

by Huakun Chen, Yongxi Lyu, Jingping Shi and Weiguo Zhang

Drones 2024, 8(10), 534; https://doi.org/10.3390/drones8100534 - 29 Sep 2024

Viewed by 1139

Abstract

Unmanned aerial vehicles (UAVs) are becoming more widely used in various industries, raising growing concerns about their safety and reliability. The flight data of UAVs can directly reflect their flight health status; however, the rarity of abnormal flight data and the spatiotemporal characteristics [...] Read more.

Unmanned aerial vehicles (UAVs) are becoming more widely used in various industries, raising growing concerns about their safety and reliability. The flight data of UAVs can directly reflect their flight health status; however, the rarity of abnormal flight data and the spatiotemporal characteristics of these data represent a significant challenge for constructing accurate and reliable anomaly detectors. To address this, this study proposes an anomaly detection framework that fully considers the temporal correlations and distribution characteristics of flight data. This framework first combines a one-dimensional convolutional neural network (1DCNN) with an autoencoder (AE) to establish a feature extraction model. This model leverages the feature extraction capabilities of the 1DCNN and the reconstruction capabilities of the AE to thoroughly extract the spatiotemporal features from UAV flight data. Then, to address the challenge of adaptive anomaly detection thresholds, this research proposes a nonlinear model of support vector data description (SVDD) utilizing a 0/1 soft-margin loss, referred to as L0/1-SVDD. This model replaces the traditional hinge loss function in SVDD with a 0/1 loss function, with the goal of enhancing the accuracy and robustness of anomaly detection. Since the 0/1 loss function is a bounded, non-convex, and non-continuous function, this paper proposes the Bregman ADMM algorithm to solve the L0/1-SVDD. Finally, the difference between the reconstructed and the actual value is employed to train the L0/1-SVDD, resulting in a hypersphere classifier that is capable of detecting UAV anomaly data. The experimental results using real flight data show that, compared with methods such as AE, LSTM, and LSTM-AE, the proposed method exhibits superior performance across five evaluation metrics. Full article

(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV)

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24 pages, 2984 KiB

Open AccessArticle

SSRL-UAVs: A Self-Supervised Deep Representation Learning Approach for GPS Spoofing Attack Detection in Small Unmanned Aerial Vehicles

by Abed Alanazi

Drones 2024, 8(9), 515; https://doi.org/10.3390/drones8090515 - 23 Sep 2024

Viewed by 1364

Abstract

Self-Supervised Representation Learning (SSRL) has become a potent strategy for addressing the growing threat of Global Positioning System (GPS) spoofing to small Unmanned Aerial Vehicles (UAVs) by capturing more abstract and high-level contributing features. This study focuses on enhancing attack detection capabilities by [...] Read more.

Self-Supervised Representation Learning (SSRL) has become a potent strategy for addressing the growing threat of Global Positioning System (GPS) spoofing to small Unmanned Aerial Vehicles (UAVs) by capturing more abstract and high-level contributing features. This study focuses on enhancing attack detection capabilities by incorporating SSRL techniques. An innovative hybrid architecture integrates Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models to detect attacks on small UAVs alongside two additional architectures, LSTM-Recurrent Neural Network (RNN) and Deep Neural Network (DNN), for detecting GPS spoofing attacks. The proposed model leverages SSRL, autonomously extracting meaningful features without the need for many labelled instances. Key configurations include LSTM-GRU, with 64 neurons in the input and concatenate layers and 32 neurons in the second layer. Ablation analysis explores various parameter settings, with the model achieving an impressive 99.9% accuracy after 10 epoch iterations, effectively countering GPS spoofing attacks. To further enhance this approach, transfer learning techniques are also incorporated, which help to improve the adaptability and generalisation of the SSRL model. By saving and applying pre-trained weights to a new dataset, we leverage prior knowledge to improve performance. This integration of SSRL and transfer learning yields a validation accuracy of 79.0%, demonstrating enhanced generalisation to new data and reduced training time. The combined approach underscores the robustness and efficiency of GPS spoofing detection in UAVs. Full article

(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV)

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17 pages, 4918 KiB

Open AccessArticle

Radio Frequency Signal-Based Drone Classification with Frequency Domain Gramian Angular Field and Convolutional Neural Network

by Yuanhua Fu and Zhiming He

Drones 2024, 8(9), 511; https://doi.org/10.3390/drones8090511 - 21 Sep 2024

Cited by 1 | Viewed by 2183

Abstract

Over the past few years, drones have been utilized in a wide range of applications. However, the illegal operation of drones may pose a series of security risks to sensitive areas such as airports and military bases. Hence, it is vital to develop [...] Read more.

Over the past few years, drones have been utilized in a wide range of applications. However, the illegal operation of drones may pose a series of security risks to sensitive areas such as airports and military bases. Hence, it is vital to develop an effective method of identifying drones to address the above issues. Existing drone classification methods based on radio frequency (RF) signals have low accuracy or a high computational cost. In this paper, we propose a novel RF signal image representation scheme that incorporates a convolutional neural network (CNN), named the frequency domain Gramian Angular Field with a CNN (FDGAF-CNN), to perform drone classification. Specifically, we first compute the time–frequency spectrum of raw RF signals based on short-time Fourier transform (STFT). Then, the 1D frequency spectrum series is encoded as 2D images using a modified GAF transform. Moreover, to further improve the recognition performance, the images obtained from different channels are fused to serve as the input of a CNN classifier. Finally, numerous experiments were conducted on the two available open-source DroneRF and DroneRFa datasets. The experimental results show that the proposed FDGAF-CNN can achieve a relatively high classification accuracy of 98.72% and 98.67% on the above two datasets, respectively, confirming the effectiveness and generalization ability of the proposed method. Full article

(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV)

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23 pages, 36929 KiB

Open AccessArticle

Dynamic Target Tracking and Following with UAVs Using Multi-Target Information: Leveraging YOLOv8 and MOT Algorithms

by Diogo Ferreira and Meysam Basiri

Drones 2024, 8(9), 488; https://doi.org/10.3390/drones8090488 - 14 Sep 2024

Cited by 1 | Viewed by 3321

Abstract

This work presents an autonomous vision-based mobile target tracking and following system designed for unmanned aerial vehicles (UAVs) leveraging multi-target information. It explores the research gap in applying the most recent multi-object tracking (MOT) methods in target following scenarios over traditional single-object tracking [...] Read more.

This work presents an autonomous vision-based mobile target tracking and following system designed for unmanned aerial vehicles (UAVs) leveraging multi-target information. It explores the research gap in applying the most recent multi-object tracking (MOT) methods in target following scenarios over traditional single-object tracking (SOT) algorithms. The system integrates the real-time object detection model, You Only Look Once (YOLO)v8, with the MOT algorithms BoT-SORT and ByteTrack, extracting multi-target information. It leverages this information to improve redetection capabilities, addressing target misidentifications (ID changes), and partial and full occlusions in dynamic environments. A depth sensing module is incorporated to enhance distance estimation when feasible. A 3D flight control system is proposed for target following, capable of reacting to changes in target speed and direction while maintaining line-of-sight. The system is initially tested in simulation and then deployed in real-world scenarios. Results show precise target tracking and following, resilient to partial and full occlusions in dynamic environments, effectively distinguishing the followed target from bystanders. A comparison between the BoT-SORT and ByteTrack trackers reveals a trade-off between computational efficiency and tracking precision. In overcoming the presented challenges, this work enables new practical applications in the field of vision-based target following from UAVs leveraging multi-target information. Full article

(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV)

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19 pages, 2093 KiB

Open AccessArticle

A DDoS Tracking Scheme Utilizing Adaptive Beam Search with Unmanned Aerial Vehicles in Smart Grid

by Wei Guo, Zhi Zhang, Liyuan Chang, Yue Song and Liuguo Yin

Drones 2024, 8(9), 437; https://doi.org/10.3390/drones8090437 - 28 Aug 2024

Cited by 1 | Viewed by 1470

Abstract

As IoT technology advances, the smart grid (SG) has become crucial to industrial infrastructure. However, SG faces security challenges, particularly from distributed denial of service (DDoS) attacks, due to inadequate security mechanisms for IoT devices. Moreover, the extensive deployment of SG exposes communication [...] Read more.

As IoT technology advances, the smart grid (SG) has become crucial to industrial infrastructure. However, SG faces security challenges, particularly from distributed denial of service (DDoS) attacks, due to inadequate security mechanisms for IoT devices. Moreover, the extensive deployment of SG exposes communication links to attacks, potentially disrupting communications and power supply. Link flooding attacks (LFAs) targeting congested backbone links have increasingly become a focal point of DDoS attacks. To address LFAs, we propose integrating unmanned aerial vehicles (UAVs) into the Smart Grid (SG) to offer a three-dimensional defense perspective. This strategy includes enhancing the speed and accuracy of attack path tracking as well as alleviating communication congestion. Therefore, our new DDoS tracking scheme leverages UAV mobility and employs beam search with adaptive beam width to reconstruct attack paths and pinpoint attack sources. This scheme features a threshold iterative update mechanism that refines the threshold each round based on prior results, improving attack path reconstruction accuracy. An adaptive beam width method evaluates the number of abnormal nodes based on the current threshold, enabling precise tracking of multiple attack paths and enhancing scheme automation. Additionally, our path-checking and merging method optimizes path reconstruction by merging overlapping paths and excluding previously searched nodes, thus avoiding redundant searches and infinite loops. Simulation results on the Keysight Ixia platform demonstrate a 98.89% attack path coverage with a minimal error tracking rate of 2.05%. Furthermore, simulations on the NS-3 platform show that drone integration not only bolsters security but also significantly enhances network performance, with communication effectiveness improving by 88.05% and recovering to 82.70% of normal levels under attack conditions. Full article

(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV)

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23 pages, 5344 KiB

Open AccessArticle

MobileAmcT: A Lightweight Mobile Automatic Modulation Classification Transformer in Drone Communication Systems

by Hongyun Fei, Baiyang Wang, Hongjun Wang, Ming Fang, Na Wang, Xingping Ran, Yunxia Liu and Min Qi

Drones 2024, 8(8), 357; https://doi.org/10.3390/drones8080357 - 30 Jul 2024

Cited by 1 | Viewed by 973

Abstract

With the rapid advancement of wireless communication technology, automatic modulation classification (AMC) plays a crucial role in drone communication systems, ensuring reliable and efficient communication in various non-cooperative environments. Deep learning technology has demonstrated significant advantages in the field of AMC, effectively and [...] Read more.

With the rapid advancement of wireless communication technology, automatic modulation classification (AMC) plays a crucial role in drone communication systems, ensuring reliable and efficient communication in various non-cooperative environments. Deep learning technology has demonstrated significant advantages in the field of AMC, effectively and accurately extracting and classifying modulation signal features. However, existing deep learning models often have high computational costs, making them difficult to deploy on resource-constrained drone communication devices. To address this issue, this study proposes a lightweight Mobile Automatic Modulation Classification Transformer (MobileAmcT). This model combines the advantages of lightweight convolutional neural networks and efficient Transformer modules, incorporating the Token and Channel Conv (TCC) module and the EfficientShuffleFormer module to enhance the accuracy and efficiency of the automatic modulation classification task. The TCC module, based on the MetaFormer architecture, integrates lightweight convolution and channel attention mechanisms, significantly improving local feature extraction efficiency. Additionally, the proposed EfficientShuffleFormer innovatively improves the traditional Transformer architecture by adopting Efficient Additive Attention and a novel ShuffleConvMLP feedforward network, effectively enhancing the global feature representation and fusion capabilities of the model. Experimental results on the RadioML2016.10a dataset show that compared to MobileNet-V2 (CNN-based) and MobileViT-XS (ViT-based), MobileAmcT reduces the parameter count by 74% and 65%, respectively, and improves classification accuracy by 1.7% and 1.09% under different SNR conditions, achieving an accuracy of 62.93%. This indicates that MobileAmcT can maintain high classification accuracy while significantly reducing the parameter count and computational complexity, clearly outperforming existing state-of-the-art AMC methods and other lightweight deep learning models. Full article

(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV)

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21 pages, 4671 KiB

Open AccessArticle

EDGS-YOLOv8: An Improved YOLOv8 Lightweight UAV Detection Model

by Min Huang, Wenkai Mi and Yuming Wang

Drones 2024, 8(7), 337; https://doi.org/10.3390/drones8070337 - 20 Jul 2024

Cited by 7 | Viewed by 3690

Abstract

In the rapidly developing drone industry, drone use has led to a series of safety hazards in both civil and military settings, making drone detection an increasingly important research field. It is difficult to overcome this challenge with traditional object detection solutions. Based [...] Read more.

In the rapidly developing drone industry, drone use has led to a series of safety hazards in both civil and military settings, making drone detection an increasingly important research field. It is difficult to overcome this challenge with traditional object detection solutions. Based on YOLOv8, we present a lightweight, real-time, and accurate anti-drone detection model (EDGS-YOLOv8). This is performed by improving the model structure, introducing ghost convolution in the neck to reduce the model size, adding efficient multi-scale attention (EMA), and improving the detection head using DCNv2 (deformable convolutional net v2). The proposed method is evaluated using two UAV image datasets, DUT Anti-UAV and Det-Fly, with a comparison to the YOLOv8 baseline model. The results demonstrate that on the DUT Anti-UAV dataset, EDGS-YOLOv8 achieves an AP value of 0.971, which is 3.1% higher than YOLOv8n’s mAP, while maintaining a model size of only 4.23 MB. The research findings and methods outlined here are crucial for improving target detection accuracy and developing lightweight UAV models. Full article

(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV)

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28 pages, 11266 KiB

Open AccessArticle

A New Approach to Classify Drones Using a Deep Convolutional Neural Network

by Hrishi Rakshit and Pooneh Bagheri Zadeh

Drones 2024, 8(7), 319; https://doi.org/10.3390/drones8070319 - 12 Jul 2024

Viewed by 1186

Abstract

In recent years, the widespread adaptation of Unmanned Aerial Vehicles (UAVs), commonly known as drones, among the public has led to significant security concerns, prompting intense research into drones’ classification methodologies. The swift and accurate classification of drones poses a considerable challenge due [...] Read more.

In recent years, the widespread adaptation of Unmanned Aerial Vehicles (UAVs), commonly known as drones, among the public has led to significant security concerns, prompting intense research into drones’ classification methodologies. The swift and accurate classification of drones poses a considerable challenge due to their diminutive size and rapid movements. To address this challenge, this paper introduces (i) a novel drone classification approach utilizing deep convolution and deep transfer learning techniques. The model incorporates bypass connections and Leaky ReLU activation functions to mitigate the ‘vanishing gradient problem’ and the ‘dying ReLU problem’, respectively, associated with deep networks and is trained on a diverse dataset. This study employs (ii) a custom dataset comprising both audio and visual data of drones as well as analogous objects like an airplane, birds, a helicopter, etc., to enhance classification accuracy. The integration of audio–visual information facilitates more precise drone classification. Furthermore, (iii) a new Finite Impulse Response (FIR) low-pass filter is proposed to convert audio signals into spectrogram images, reducing susceptibility to noise and interference. The proposed model signifies a transformative advancement in convolutional neural networks’ design, illustrating the compatibility of efficacy and efficiency without compromising on complexity and learnable properties. A notable performance was demonstrated by the proposed model, with an accuracy of 100% achieved on the test images using only four million learnable parameters. In contrast, the Resnet50 and Inception-V3 models exhibit 90% accuracy each on the same test set, despite the employment of 23.50 million and 21.80 million learnable parameters, respectively. Full article

(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV)

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18 pages, 6957 KiB

Open AccessArticle

Multi-Device Security Application for Unmanned Surface and Aerial Systems

by Andre Leon, Christopher Britt and Britta Hale

Drones 2024, 8(5), 200; https://doi.org/10.3390/drones8050200 - 15 May 2024

Viewed by 1131

Abstract

The use of autonomous and unmanned systems continues to increase, with uses spanning from package delivery to simple automation of tasks and from factory usage to defense industries and agricultural applications. With the proliferation of unmanned systems comes the question of how to [...] Read more.

The use of autonomous and unmanned systems continues to increase, with uses spanning from package delivery to simple automation of tasks and from factory usage to defense industries and agricultural applications. With the proliferation of unmanned systems comes the question of how to secure the command-and-control communication links among such devices and their operators. In this work, we look at the use of the Messaging Layer Security (MLS) protocol, designed to support long-lived continuous sessions and group communication with a high degree of security. We build out MAUI—an MLS API for UxS Integration that provides an interface for the secure exchange of data between a ScanEagle unmanned aerial vehicle (UAV) and an unmanned surface vehicle (USV) in a multi-domain ad-hoc network configuration, and experiment on system limits such as the ciphersuite set-up time and message handling rates. The experiments in this work were conducted in virtual and physical environments between the UAV, USV, and a controller device (all of different platforms). Our results demonstrate the viability of capitalizing on MLS’s capabilities to securely and efficiently transmit data for distributed communication among various unmanned system platforms. Full article

(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV)

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18 pages, 534 KiB

Open AccessArticle

Dual-Driven Learning-Based Multiple-Input Multiple-Output Signal Detection for Unmanned Aerial Vehicle Air-to-Ground Communications

by Haihan Li, Yongming He, Shuntian Zheng, Fan Zhou and Hongwen Yang

Drones 2024, 8(5), 180; https://doi.org/10.3390/drones8050180 - 2 May 2024

Cited by 1 | Viewed by 1589

Abstract

Unmanned aerial vehicle (UAV) air-to-ground (AG) communication plays a critical role in the evolving space–air–ground integrated network of the upcoming sixth-generation cellular network (6G). The integration of massive multiple-input multiple-output (MIMO) systems has become essential for ensuring optimal performing communication technologies. This article [...] Read more.

Unmanned aerial vehicle (UAV) air-to-ground (AG) communication plays a critical role in the evolving space–air–ground integrated network of the upcoming sixth-generation cellular network (6G). The integration of massive multiple-input multiple-output (MIMO) systems has become essential for ensuring optimal performing communication technologies. This article presents a novel dual-driven learning-based network for millimeter-wave (mm-wave) massive MIMO symbol detection of UAV AG communications. Our main contribution is that the proposed approach combines a data-driven symbol-correction network with a model-driven orthogonal approximate message passing network (OAMP-Net). Through joint training, the dual-driven network reduces symbol detection errors propagated through each iteration of the model-driven OAMP-Net. The numerical results demonstrate the superiority of the dual-driven detector over the conventional minimum mean square error (MMSE), orthogonal approximate message passing (OAMP), and OAMP-Net detectors at various noise powers and channel estimation errors. The dual-driven MIMO detector exhibits a 2–3 dB lower signal-to-noise ratio (SNR) requirement compared to the MMSE and OAMP-Net detectors to achieve a bit error rate (BER) of

1 \times 10^{- 2}

when the channel estimation error is −30 dB. Moreover, the dual-driven MIMO detector exhibits an increased tolerance to channel estimation errors by 2–3 dB to achieve a BER of

1 \times 10^{- 3}

. Full article

(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV)

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17 pages, 1208 KiB

Open AccessArticle

The Sound of Surveillance: Enhancing Machine Learning-Driven Drone Detection with Advanced Acoustic Augmentation

by Sebastian Kümmritz

Drones 2024, 8(3), 105; https://doi.org/10.3390/drones8030105 - 19 Mar 2024

Cited by 2 | Viewed by 2529

Abstract

In response to the growing challenges in drone security and airspace management, this study introduces an advanced drone classifier, capable of detecting and categorizing Unmanned Aerial Vehicles (UAVs) based on acoustic signatures. Utilizing a comprehensive database of drone sounds across EU-defined classes (C0 [...] Read more.

In response to the growing challenges in drone security and airspace management, this study introduces an advanced drone classifier, capable of detecting and categorizing Unmanned Aerial Vehicles (UAVs) based on acoustic signatures. Utilizing a comprehensive database of drone sounds across EU-defined classes (C0 to C3), this research leverages machine learning (ML) techniques for effective UAV identification. The study primarily focuses on the impact of data augmentation methods—pitch shifting, time delays, harmonic distortion, and ambient noise integration—on classifier performance. These techniques aim to mimic real-world acoustic variations, thus enhancing the classifier’s robustness and practical applicability. Results indicate that moderate levels of augmentation significantly improve classification accuracy. However, excessive application of these methods can negatively affect performance. The study concludes that sophisticated acoustic data augmentation can substantially enhance ML-driven drone detection, providing a versatile and efficient tool for managing drone-related security risks. This research contributes to UAV detection technology, presenting a model that not only identifies but also categorizes drones, underscoring its potential for diverse operational environments. Full article

(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV)

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29 pages, 10590 KiB

Open AccessArticle

GA-Net: Accurate and Efficient Object Detection on UAV Images Based on Grid Activations

by Ruiyi Zhang, Bin Luo, Xin Su and Jun Liu

Drones 2024, 8(3), 74; https://doi.org/10.3390/drones8030074 - 21 Feb 2024

Cited by 3 | Viewed by 2113

Abstract

Object detection plays a crucial role in unmanned aerial vehicle (UAV) missions, where captured objects are often small and require high-resolution processing. However, this requirement is always in conflict with limited computing resources, vast fields of view, and low latency requirements. To tackle [...] Read more.

Object detection plays a crucial role in unmanned aerial vehicle (UAV) missions, where captured objects are often small and require high-resolution processing. However, this requirement is always in conflict with limited computing resources, vast fields of view, and low latency requirements. To tackle these issues, we propose GA-Net, a novel approach tailored for UAV images. The key innovation includes the Grid Activation Module (GAM), which efficiently calculates grid activations, the probability of foreground presence at grid scale. With grid activations, the GAM helps filter out patches without objects, minimize redundant computations, and improve inference speeds. Additionally, the Grid-based Dynamic Sample Selection (GDSS) focuses the model on discriminating positive samples and hard negatives, addressing background bias during training. Further enhancements involve GhostFPN, which refines Feature Pyramid Network (FPN) using Ghost module and depth-wise separable convolution. This not only expands the receptive field for improved accuracy, but also reduces computational complexity. We conducted comprehensive evaluations on DGTA-Cattle-v2, a synthetic dataset with added background images, and three public datasets (VisDrone, SeaDronesSee, DOTA) from diverse domains. The results prove the effectiveness and practical applicability of GA-Net. Despite the common accuracy and speed trade-off challenge, our GA-Net successfully achieves a mutually beneficial scenario through the strategic use of grid activations. Full article

(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV)

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20 pages, 6066 KiB

Open AccessArticle

Smart Drone Surveillance System Based on AI and on IoT Communication in Case of Intrusion and Fire Accident

by Minh Long Hoang

Drones 2023, 7(12), 694; https://doi.org/10.3390/drones7120694 - 2 Dec 2023

Cited by 14 | Viewed by 10082

Abstract

Research on developing a smart security system is based on Artificial Intelligence with an unmanned aerial vehicle (UAV) to detect and monitor alert situations, such as fire accidents and theft/intruders in the building or factory, which is based on the Internet of Things [...] Read more.

Research on developing a smart security system is based on Artificial Intelligence with an unmanned aerial vehicle (UAV) to detect and monitor alert situations, such as fire accidents and theft/intruders in the building or factory, which is based on the Internet of Things (IoT) network. The system includes a Passive Pyroelectric Infrared Detector for human detection and an analog flame sensor to sense the appearance of the concerned objects and then transmit the signal to the workstation via Wi-Fi based on the microcontroller Espressif32 (Esp32). The computer vision models YOLOv8 (You Only Look Once version 8) and Cascade Classifier are trained and implemented into the workstation, which is able to identify people, some potentially dangerous objects, and fire. The drone is also controlled by three algorithms—distance maintenance, automatic yaw rotation, and potentially dangerous object avoidance—with the support of a proportional–integral–derivative (PID) controller. The Smart Drone Surveillance System has good commands for automatic tracking and streaming of the video of these specific circumstances and then transferring the data to the involved parties such as security or staff. Full article

(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV)

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18 pages, 6706 KiB

Open AccessArticle

Detection of Volatile Organic Compounds (VOCs) in Indoor Environments Using Nano Quadcopter

by Aline Mara Oliveira, Aniel Silva Morais, Gabriela Vieira Lima, Rafael Monteiro Jorge Alves Souza and Luis Cláudio Oliveira-Lopes

Drones 2023, 7(11), 660; https://doi.org/10.3390/drones7110660 - 6 Nov 2023

Cited by 2 | Viewed by 2264

Abstract

The dispersion of chemical gases poses a threat to human health, animals, and the environment. Leaks or accidents during the handling of samples and laboratory materials can result in the uncontrolled release of hazardous or explosive substances. Therefore, it is crucial to monitor [...] Read more.

The dispersion of chemical gases poses a threat to human health, animals, and the environment. Leaks or accidents during the handling of samples and laboratory materials can result in the uncontrolled release of hazardous or explosive substances. Therefore, it is crucial to monitor gas concentrations in environments where these substances are manipulated. Gas sensor technology has evolved rapidly in recent years, offering increasingly precise and reliable solutions. However, there are still challenges to be overcome, especially when sensors are deployed on unmanned aerial vehicles (UAVs). This article discusses the use of UAVs to locate gas sources and presents real test results using the SGP40 metal oxide semiconductor gas sensor onboard the Crazyflie 2.1 nano quadcopter. The solution proposed in this article uses an odor source identification strategy, employing a gas distribution mapping approach in a three-dimensional environment. The aim of the study was to investigate the feasibility and effectiveness of this approach for detecting gases in areas that are difficult to access or dangerous for humans. The results obtained show that the use of drones equipped with gas sensors is a promising alternative for the detection and monitoring of gas leaks in closed environments. Full article

(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV)

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Other

Jump to: Research

17 pages, 29722 KiB

Open AccessTechnical Note

DLSW-YOLOv8n: A Novel Small Maritime Search and Rescue Object Detection Framework for UAV Images with Deformable Large Kernel Net

by Zhumu Fu, Yuehao Xiao, Fazhan Tao, Pengju Si and Longlong Zhu

Drones 2024, 8(7), 310; https://doi.org/10.3390/drones8070310 - 9 Jul 2024

Cited by 2 | Viewed by 1235

Abstract

Unmanned aerial vehicle maritime search and rescue target detection is susceptible to external factors, which can seriously reduce detection accuracy. To address these challenges, the DLSW-YOLOv8n algorithm is proposed combining Deformable Large Kernel Net (DL-Net), SPD-Conv, and WIOU. Firstly, to refine the contextual [...] Read more.

Unmanned aerial vehicle maritime search and rescue target detection is susceptible to external factors, which can seriously reduce detection accuracy. To address these challenges, the DLSW-YOLOv8n algorithm is proposed combining Deformable Large Kernel Net (DL-Net), SPD-Conv, and WIOU. Firstly, to refine the contextual understanding ability of the model, the DL-Net is integrated into the C2f module of the backbone network. Secondly, to enhance the small target characterization representation, a spatial-depth layer is used instead of pooling in the convolution module, and an additional detection head is integrated into the low-level feature map. The loss function is improved to enhance small target localization performance. Finally, a UAV maritime target detection dataset is employed to demonstrate the effectiveness of the proposed algorithm, whose results show that DLSW-YOLOv8n achieves a detection accuracy of 79.5%, which represents an improvement of 13.1% compared to YOLOv8n. Full article

(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV)

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