A Deep Learning Approach to Classify Drones and Birds

Selvi, S. Sethu; Pavithraa, S; Dharini, R; Chaitra, E

doi:10.1109/mysurucon55714.2022.9972589

Cited by 4 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, many research works have been published to address UAV detection, tracking, and classification problems. The main drone detection technologies are: radar sensors [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ], RF sensors [ 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ], audio sensors [ 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ], and camera sensors using visual UAV characteristics [ 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 ]. Based on the above-mentioned sources, the advantages and disadvantages of each drone detection technology are compared in Table 2 .…”

Section: Drone Detection Technologiesmentioning

confidence: 99%

“…Experimental results demonstrated that all evaluation metric values for improved YOLOv5 outperformed the baseline model. Drone and bird detection based on YOLOv4 and YOLOv5 was conducted in [ 64 ]. A custom dataset compiled from several online sources consists of 900 images: 664 images for drones and 236 images for birds.…”

Section: Drone Detection Technologiesmentioning

confidence: 99%

“…The main steps of the image acquisition process are: energy is reflected from the UAV, the reflected energy is focused by an optical system, and a camera sensor measures the amount of energy.Related works on drone object detection based on visual data can be divided into two major categories depending on whether the authors employed a deep learning model or dealt with feature extraction using traditional handcrafted features. However, recent studies on visual-based drone detection and identification have mostly employed learned features or deep learning models[61][62][63][64][65][66][67][68][69][70][71]. Deep learning for object detection with convolutional neural networks (CNNs) has two approaches: one-stage detection and two-stage detection.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Advances and Challenges in Drone Detection and Classification Techniques: A State-of-the-Art Review

Seidaliyeva,

Ilipbayeva,

Taissariyeva

et al. 2023

Sensors

View full text Add to dashboard Cite

The fast development of unmanned aerial vehicles (UAVs), commonly known as drones, has brought a unique set of opportunities and challenges to both the civilian and military sectors. While drones have proven useful in sectors such as delivery, agriculture, and surveillance, their potential for abuse in illegal airspace invasions, privacy breaches, and security risks has increased the demand for improved detection and classification systems. This state-of-the-art review presents a detailed overview of current improvements in drone detection and classification techniques: highlighting novel strategies used to address the rising concerns about UAV activities. We investigate the threats and challenges faced due to drones’ dynamic behavior, size and speed diversity, battery life, etc. Furthermore, we categorize the key detection modalities, including radar, radio frequency (RF), acoustic, and vision-based approaches, and examine their distinct advantages and limitations. The research also discusses the importance of sensor fusion methods and other detection approaches, including wireless fidelity (Wi-Fi), cellular, and Internet of Things (IoT) networks, for improving the accuracy and efficiency of UAV detection and identification.

show abstract

Section: Drone Detection Technologiesmentioning

confidence: 99%

Section: Drone Detection Technologiesmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Advances and Challenges in Drone Detection and Classification Techniques: A State-of-the-Art Review

Seidaliyeva,

Ilipbayeva,

Taissariyeva

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…They obtained an f1 score of 98% with YOLOv4 and 94% with YOLOv5. The YOLOv4 model achieved a detection speed of 54 fps on GPU and 12 fps on CPU, while the YOLOv5 model achieved a detection speed of 77 fps on GPU and 27 fps on CPU [27].…”

Section: Introductionmentioning

confidence: 99%

Classification of Unmanned Aerial Vehicle and Bird Images Using Deep Transfer Learning Methods

Özdemir,

OZKAN

2023

icat

View full text Add to dashboard Cite

The increasing accessibility and affordability of unmanned aerial vehicles (UAVs), commonly known as drones, have led to the emergence of malicious users. In precaution to this perceived threat, various anti-UAV systems are being developed, including electro-optical systems utilizing cameras. It is possible to detect UAVs from images using various machine learning methods. However, the similarity between UAVs and birds poses a challenge, as birds can be mistakenly identified as UAVs, leading to false alarms in a security system. In order to avoid this problem, this study provided the classification of birds and unmanned aerial vehicles over images using deep learning methods. In this study, a data set consisting of 400 birds and 428 UAV images was used. The data were divided into 70% for training, 30% for testing and validation purposes. Three different deep learning models, based on DenseNet, VGG16, and VGG19 architectures, were trained using transfer learning techniques, and their performances were compared. Experimental results on the test data showed an accuracy of 94.64% with the DenseNet model, 89.67% with the VGG16 model, and 90.67% with the VGG19 model.

show abstract

Efficient Drones-Birds Classification Using Transfer Learning

Mohamed,

Alharbi

2023

2023 4th International Conference on Artificial Intelligence, Robotics and Control (AIRC)

View full text Add to dashboard Cite

A Deep Learning Approach to Classify Drones and Birds

Cited by 4 publications

References 11 publications

Advances and Challenges in Drone Detection and Classification Techniques: A State-of-the-Art Review

Advances and Challenges in Drone Detection and Classification Techniques: A State-of-the-Art Review

Classification of Unmanned Aerial Vehicle and Bird Images Using Deep Transfer Learning Methods

Efficient Drones-Birds Classification Using Transfer Learning

Contact Info

Product

Resources

About