Detection of loaded and unloaded UAV using deep neural network

Seidaliyeva, Ulzhalgas; Alduraibi, Manal; Ilipbayeva, Lyazzat; Almagambetov, Akhan

doi:10.1109/irc.2020.00093

Cited by 30 publications

(20 citation statements)

References 16 publications

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“…A model that attempts to classify loaded and unloaded drones [15], reports a mean average precision of 75.0%. The model uses the YOLOv2 object detection model.…”

Section: Resultsmentioning

confidence: 99%

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Drone Model Identification by Convolutional Neural Network from Video Stream

Wiśniewski

Rana

Petrunin

2021

2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC)

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We present a convolutional neural network model that correctly identifies drone models in real-life video streams of flying drones. To achieve this, we show a method of generating synthetic drone images. To create a diverse dataset, the simulation parameters (such as drone textures, lighting, and orientation) are randomized. This synthetic dataset is used to train a convolutional neural network to identify the drone model: DJI Phantom, DJI Mavic, or DJI Inspire. The model is then tested on a real-life Anti-UAV dataset of flying drones. The benchmark results show that the DenseNet201 architecture performed the best. Adding Gaussian noise to the training dataset and performing full training (as opposed to freezing layers) shows the best results. The model shows an average accuracy of 92.4%, and an average precision of 88.6% on the test dataset.

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“…A model that attempts to classify loaded and unloaded drones [15], reports a mean average precision of 75.0%. The model uses the YOLOv2 object detection model.…”

Section: Resultsmentioning

confidence: 99%

“…Another method [14] uses the YOLOv3 regional proposal network to predict whether the drone is a tricopter, quadcopter, or a hexacopter. Lastly, a method shown in [15] attempts to classify whether the drone is carrying a payload using YOLOv2.…”

Section: Introductionmentioning

confidence: 99%

Drone Model Identification by Convolutional Neural Network from Video Stream

Wiśniewski

Rana

Petrunin

2021

2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC)

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“…The use of the YOLOv3 deep learning network in this study has resulted in improved accuracy and precision of drone detection compared to other methods due to its lightweight architecture and appropriate depth. In 2020, drones were detected using YOLOv4 [48], YOLOv3 [21,48], YOLOv2 [20], tiny-YOLOv3 [49], Fast-RCNN [49], and SSD [48] networks and the results were compared [48]. The three models YOLOv4, YOLOv3, and SSD were compared, and, respectively, YOLOv4, YOLOv3, and SSD had the best accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…In the last decade, deep learning networks have become the best model for visual processing, such as object detection and tracking [20][21][22][23]. Object detection using deep learning networks has received much attention due to its higher computational power and accuracy [24].…”

Section: Introductionmentioning

confidence: 99%

Detection and Recognition of Drones Based on a Deep Convolutional Neural Network Using Visible Imagery

et al. 2022

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Drones are becoming increasingly popular not only for recreational purposes but also in a variety of applications in engineering, disaster management, logistics, securing airports, and others. In addition to their useful applications, an alarming concern regarding physical infrastructure security, safety, and surveillance at airports has arisen due to the potential of their use in malicious activities. In recent years, there have been many reports of the unauthorized use of various types of drones at airports and the disruption of airline operations. To address this problem, this study proposes a novel deep learning-based method for the efficient detection and recognition of two types of drones and birds. Evaluation of the proposed approach with the prepared image dataset demonstrates better efficiency compared to existing detection systems in the literature. Furthermore, drones are often confused with birds because of their physical and behavioral similarity. The proposed method is not only able to detect the presence or absence of drones in an area but also to recognize and distinguish between two types of drones, as well as distinguish them from birds. The dataset used in this work to train the network consists of 10,000 visible images containing two types of drones as multirotors, helicopters, and also birds. The proposed deep learning method can directly detect and recognize two types of drones and distinguish them from birds with an accuracy of 83%, mAP of 84%, and IoU of 81%. The values of average recall, average accuracy, and average F1-score were also reported as 84%, 83%, and 83%, respectively, in three classes.

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“…Recent advances in deep convolutional neural networks and the appearance of more improved hardware make it possible to use visual information to recognize objects with higher accuracy and speed [17]. Unlike conventional drone detection technologies, the nature of deep learning networks is to perform drone recognition simultaneously.…”

Section: Introductionmentioning

confidence: 99%

A Modified YOLOv4 Deep Learning Network for Vision-Based UAV Recognition

Javan

Samadzadegan

Gholamshahi

et al. 2022

Drones

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The use of drones in various applications has now increased, and their popularity among the general public has increased. As a result, the possibility of their misuse and their unauthorized intrusion into important places such as airports and power plants are increasing, threatening public safety. For this reason, accurate and rapid recognition of their types is very important to prevent their misuse and the security problems caused by unauthorized access to them. Performing this operation in visible images is always associated with challenges, such as the small size of the drone, confusion with birds, the presence of hidden areas, and crowded backgrounds. In this paper, a novel and accurate technique with a change in the YOLOv4 network is presented to recognize four types of drones (multirotors, fixed-wing, helicopters, and VTOLs) and to distinguish them from birds using a set of 26,000 visible images. In this network, more precise and detailed semantic features were extracted by changing the number of convolutional layers. The performance of the basic YOLOv4 network was also evaluated on the same dataset, and the proposed model performed better than the basic network in solving the challenges. Compared to the basic YOLOv4 network, the proposed model provides better performance in solving challenges. Additionally, it can perform automated vision-based recognition with a loss of 0.58 in the training phase and 83% F1-score, 83% accuracy, 83% mean Average Precision (mAP), and 84% Intersection over Union (IoU) in the testing phase. These results represent a slight improvement of 4% in these evaluation criteria over the YOLOv4 basic model.

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Detection of loaded and unloaded UAV using deep neural network

Cited by 30 publications

References 16 publications

Drone Model Identification by Convolutional Neural Network from Video Stream

Drone Model Identification by Convolutional Neural Network from Video Stream

Detection and Recognition of Drones Based on a Deep Convolutional Neural Network Using Visible Imagery

A Modified YOLOv4 Deep Learning Network for Vision-Based UAV Recognition

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