DronAID : A Smart Human Detection Drone for Rescue

Tariq, Rameesha; Rahim, M Asifur; Aslam, Nimra; Bawany, Narmeen Zakaria; Faseeha, Ummay

doi:10.1109/honet.2018.8551326

Cited by 51 publications

(16 citation statements)

References 7 publications

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“…The most critical requirement for search-and-rescue teams to be able to save people's lives is having high-performance communication links. The authors of [11][12][13][14][15] focused on proposing different propagation models that could provide robust wireless communication between aerial stations and interdepartmental headquarters, including rescue teams. Hata and Okumura are typical empirical models, while air-to-ground and free-space are typical deterministic propagation models.…”

Section: Related Workmentioning

confidence: 99%

Modifying Hata-Davidson Propagation Model for Remote Sensing in Complex Environments Using a Multifactional Drone

Almalki

Soufiene

2022

Sensors

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The coupling of drones and IoT is a major topics in academia and industry since it significantly contributes towards making human life safer and smarter. Using drones is seen as a robust approach for mobile remote sensing operations, such as search-and-rescue missions, due to their speed and efficiency, which could seriously affect victims’ chances of survival. This paper aims to modify the Hata-Davidson empirical propagation model based on RF drone measurement to conduct searches for missing persons in complex environments with rugged areas after manmade or natural disasters. A drone was coupled with a thermal FLIR lepton camera, a microcontroller, GPS, and weather station sensors. The proposed modified model utilized the least squares tuning algorithm to fit the data measured from the drone communication system. This enhanced the RF connectivity between the drone and the local authority, as well as leading to increased coverage footprint and, thus, the performance of wider search-and-rescue operations in a timely fashion using strip search patterns. The development of the proposed model considered both software simulation and hardware implementations. Since empirical propagation models are the most adjustable models, this study concludes with a comparison between the modified Hata-Davidson algorithm against other well-known modified empirical models for validation using root mean square error (RMSE). The experimental results show that the modified Hata-Davidson model outperforms the other empirical models, which in turn helps to identify missing persons and their locations using thermal imaging and a GPS sensor.

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Section: Related Workmentioning

confidence: 99%

Modifying Hata-Davidson Propagation Model for Remote Sensing in Complex Environments Using a Multifactional Drone

Almalki

Soufiene

2022

Sensors

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“…For example, be studied in fields such as automatic license plate recognition [5]; detection of the diseased plant [6]; traffic light detector for self-driving vehicles [7,8]; for violent individual identification [9]; and detector for ship detection in SAR Images [10]. Searching for missing objects in a disaster situation or used in operational missions in war situations, and it is necessary in a situation where medical staff can quickly find injured people at the accident site [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Improved YOLOv5: Efficient Object Detection Using Drone Images under Various Conditions

2022

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With the recent development of drone technology, object detection technology is emerging, and these technologies can also be applied to illegal immigrants, industrial and natural disasters, and missing people and objects. In this paper, we would like to explore ways to increase object detection performance in these situations. Photography was conducted in an environment where it was confusing to detect an object. The experimental data were based on photographs that created various environmental conditions, such as changes in the altitude of the drone, when there was no light, and taking pictures in various conditions. All the data used in the experiment were taken with F11 4K PRO drone and VisDrone dataset. In this study, we propose an improved performance of the original YOLOv5 model. We applied the obtained data to each model: the original YOLOv5 model and the improved YOLOv5_Ours model, to calculate the key indicators. The main indicators are precision, recall, F-1 score, and mAP (0.5), and the YOLOv5_Ours values of mAP (0.5) and function loss were improved by comparing it with the original YOLOv5 model. Finally, the conclusion was drawn based on the data comparing the original YOLOv5 model and the improved YOLOv5_Ours model. As a result of the analysis, we were able to arrive at a conclusion on the best model of object detection under various conditions.

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“…In recent years, drones, also known as Unmanned Aerial Vehicles (UAVs), have become significantly popular due to the rapid technical enhancements in both their hardware, by equipping them with cameras and audio recording technologies, as well as their software, by providing the support of autonomous flying and human tracking capabilities. Initially, drones were mainly used for cinematography and recreational purposes; however, their usage has been extended to automate day-to-day operations such as vegetation monitoring [ 1 ], various wildfire mapping applications [ 2 ], precision agriculture [ 3 ] and flying over dangerous and out-of-reach areas for search and rescue missions [ 4 ]. In addition to their useful applications, their use in malicious activities to invade privacy, security and safety regulations has been increasing alarmingly.…”

Section: Introductionmentioning

confidence: 99%

Audio-Based Drone Detection and Identification Using Deep Learning Techniques with Dataset Enhancement through Generative Adversarial Networks

Al-Emadi

Al-Ali

Al‐Ali

2021

Sensors

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Drones are becoming increasingly popular not only for recreational purposes but in day-to-day applications in engineering, medicine, logistics, security and others. In addition to their useful applications, an alarming concern in regard to the physical infrastructure security, safety and privacy has arisen due to the potential of their use in malicious activities. To address this problem, we propose a novel solution that automates the drone detection and identification processes using a drone’s acoustic features with different deep learning algorithms. However, the lack of acoustic drone datasets hinders the ability to implement an effective solution. In this paper, we aim to fill this gap by introducing a hybrid drone acoustic dataset composed of recorded drone audio clips and artificially generated drone audio samples using a state-of-the-art deep learning technique known as the Generative Adversarial Network. Furthermore, we examine the effectiveness of using drone audio with different deep learning algorithms, namely, the Convolutional Neural Network, the Recurrent Neural Network and the Convolutional Recurrent Neural Network in drone detection and identification. Moreover, we investigate the impact of our proposed hybrid dataset in drone detection. Our findings prove the advantage of using deep learning techniques for drone detection and identification while confirming our hypothesis on the benefits of using the Generative Adversarial Networks to generate real-like drone audio clips with an aim of enhancing the detection of new and unfamiliar drones.

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DronAID : A Smart Human Detection Drone for Rescue

Cited by 51 publications

References 7 publications

Modifying Hata-Davidson Propagation Model for Remote Sensing in Complex Environments Using a Multifactional Drone

Modifying Hata-Davidson Propagation Model for Remote Sensing in Complex Environments Using a Multifactional Drone

Improved YOLOv5: Efficient Object Detection Using Drone Images under Various Conditions

Audio-Based Drone Detection and Identification Using Deep Learning Techniques with Dataset Enhancement through Generative Adversarial Networks

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