Geo-location Based Augmented Reality Application For Cultural Heritage Using Drones

Ünal, Metehan; Bostancı, Erkan; Sertalp, Evren; Güzel, Mehmet Serdar; Kanwal, Nadia

doi:10.1109/ismsit.2018.8567073

Cited by 19 publications

(13 citation statements)

References 8 publications

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“…Forest fires have been intensified in some provinces. It is known that helicopters are involved in firefighting besides water tankers and pumper vehicles and so faster results have been obtained from land vehicles [11].…”

Section: Methodsmentioning

confidence: 99%

Machine Learning–Based Early Fire Detection System Using a Low-Cost Drone

Yanık¹,

Yanık²,

Güzel³

et al. 2022

Advanced Sensing in Image Processing and IoT

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This paper proposes a new machine learning based system for forest fire earlier detection in a low-cost and accurate manner. Accordingly, it is aimed to bring a new and definite perspective to visual detection in forest fires. A drone is constructed for this purpose. The microcontroller in the system has been programmed by training with deep learning methods, and the unmanned aerial vehicle has been given the ability to recognize the smoke, the earliest sign of fire detection. The common problem in the prevalent algorithms used in fire detection is the high false alarm and overlook rates. Confirming the result obtained from the visualization with an additional supervision stage will increase the reliability of the system as well as guarantee the accuracy of the result. Due to the mobile vision ability of the unmanned aerial vehicle, the data can be controlled from any point of view clearly and continuously. System performance are validated by conducting experiments in both simulation and physical environments.

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Section: Methodsmentioning

confidence: 99%

Machine Learning–Based Early Fire Detection System Using a Low-Cost Drone

Yanık¹,

Yanık²,

Güzel³

et al. 2022

Advanced Sensing in Image Processing and IoT

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“…Hence, a wide-range network is not required. HoloLens O X Daniar [32] HoloLens X O Jeong [27] HMD X O Zhihao [33] HMD X O Sreeram [34] Mobile Phone X O Metehan [35] PC X O Thon [36] PC To start the system, once both clients and server are connected, the server has to access a web_video_server package. This package are used to determine the IP address for video streaming before it can proceed.…”

Section: Video Streaming Systemmentioning

confidence: 99%

HoloGCS : Mixed Reality-based Ground Control Station for Unmanned Aerial Vehicle

Widiyanti¹,

Asmoro²,

Shin³

2022

Preprint

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Ground control station (GCS) is a system for controlling and monitoring unmanned aerial vehicle (UAV). In current GCS, the device used are considered as complex environment. This paper proposes a video streaming and speech command control for supporting mixed reality based UAV GCS using Microsoft HoloLens. Video streaming will inform the UAV view and transmit the raw video to the HoloLens, while the HoloLens steers the UAV based on the displayed UAV field of view (FoV). Using the HoloLens Mixed Reality Tool-Kit (MRTK) speech input, UAV speech control from the HoloLens was successfully implemented. Finally, experimental results based on video streaming and speech command calculation of the throughput, round-time trip, latency and speech accuracy tests are discussed to demonstrate the feasibility of the proposed scheme.

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“…Although the solution was not fully implemented, this research proposes the AR rendering, not only of the coastline, but from the nearby boats as well, through the connection to an automatic identification system (AIS). Unal et al [ 38 ] modeled an estimation of the Roman Bath of Ankara, an important archeological site in Turkey, which is almost in ruins, to virtually preserve the cultural heritage of the place. The experience consists in tracking a UAV through GPS and orientation sensors to simulate the flight around the real place.…”

Section: Research Backgroundmentioning

confidence: 99%

Web AR Solution for UAV Pilot Training and Usability Testing

Ribeiro

Ramos

Safadinho

et al. 2021

Sensors

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Data and services are available anywhere at any time thanks to the Internet and mobile devices. Nowadays, there are new ways of representing data through trendy technologies such as augmented reality (AR), which extends our perception of reality through the addition of a virtual layer on top of real-time images. The great potential of unmanned aerial vehicles (UAVs) for carrying out routine and professional tasks has encouraged their use in the creation of several services, such as package delivery or industrial maintenance. Unfortunately, drone piloting is difficult to learn and requires specific training. Since regular training is performed with virtual simulations, we decided to propose a multiplatform cloud-hosted solution based in Web AR for drone training and usability testing. This solution defines a configurable trajectory through virtual elements represented over barcode markers placed on a real environment. The main goal is to provide an inclusive and accessible training solution which could be used by anyone who wants to learn how to pilot or test research related to UAV control. For this paper, we reviewed drones, AR, and human–drone interaction (HDI) to propose an architecture and implement a prototype, which was built using a Raspberry Pi 3, a camera, and barcode markers. The validation was conducted using several test scenarios. The results show that a real-time AR experience for drone pilot training and usability testing is achievable through web technologies. Some of the advantages of this approach, compared to traditional methods, are its high availability by using the web and other ubiquitous devices; the minimization of technophobia related to crashes; and the development of cost-effective alternatives to train pilots and make the testing phase easier for drone researchers and developers through trendy technologies.

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Geo-location Based Augmented Reality Application For Cultural Heritage Using Drones

Cited by 19 publications

References 8 publications

Machine Learning–Based Early Fire Detection System Using a Low-Cost Drone

Machine Learning–Based Early Fire Detection System Using a Low-Cost Drone

HoloGCS : Mixed Reality-based Ground Control Station for Unmanned Aerial Vehicle

Web AR Solution for UAV Pilot Training and Usability Testing

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