Multipurpose deep learning-powered UAV for forest fire prevention and emergency response
This paper presents a customized UAV designed for rescue and safety purposes in the forest sector. The UAV features a durable F450 frame quadcopter with four 1000KV brushless motors and a KK2.1 Flight Control Board for stability and manoeuvrability with a runtime of 90 min. It incorporates a Raspber...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | HardwareX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S246806722300086X |
| _version_ | 1797386768751788032 |
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| author | Tejas Rathod Vinay Patil R. Harikrishnan Priti Shahane |
| author_facet | Tejas Rathod Vinay Patil R. Harikrishnan Priti Shahane |
| author_sort | Tejas Rathod |
| collection | DOAJ |
| description | This paper presents a customized UAV designed for rescue and safety purposes in the forest sector. The UAV features a durable F450 frame quadcopter with four 1000KV brushless motors and a KK2.1 Flight Control Board for stability and manoeuvrability with a runtime of 90 min. It incorporates a Raspberry Pi camera for real-time video streaming, enabling efficient identification of individuals in need of assistance. The GSM module allows contactless communication, ensuring streamlined and safe interaction. A motor controls the lid of the customizable first aid kit box, facilitating efficient aid delivery. The Neo-6 M GPS module provides accurate localization of the drone and individuals in distress with a horizontal position accuracy of 2.5 m. The UAV collects temperature and humidity data using the DHT 11 sensor having +/- 2 degreesC and +- 5% accuracy respectively. This sensor employs advanced deep learning models, including artificial neural networks (ANN) and generative adversarial networks (GANs), for real-time forest fire prediction with an accuracy of 90.7 % The integration of GANs enhances accuracy through synthetic data generation. Moreover, all these components are interfaced using a Raspberry Pi4 and a GUI, providing a smooth user control experience and end-to-end information for quick and effective emergency response. |
| first_indexed | 2024-03-08T22:14:08Z |
| format | Article |
| id | doaj.art-573efb85007d409489620b270b340d70 |
| institution | Directory Open Access Journal |
| issn | 2468-0672 |
| language | English |
| last_indexed | 2024-03-08T22:14:08Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | HardwareX |
| spelling | doaj.art-573efb85007d409489620b270b340d702023-12-19T04:17:13ZengElsevierHardwareX2468-06722023-12-0116e00479Multipurpose deep learning-powered UAV for forest fire prevention and emergency responseTejas Rathod0Vinay Patil1R. Harikrishnan2Priti Shahane3Symbiosis Institute of Technology, Pune Campus, Symbiosis International Deemed University, Pune, IndiaSymbiosis Institute of Technology, Pune Campus, Symbiosis International Deemed University, Pune, IndiaCorresponding author.; Symbiosis Institute of Technology, Pune Campus, Symbiosis International Deemed University, Pune, IndiaSymbiosis Institute of Technology, Pune Campus, Symbiosis International Deemed University, Pune, IndiaThis paper presents a customized UAV designed for rescue and safety purposes in the forest sector. The UAV features a durable F450 frame quadcopter with four 1000KV brushless motors and a KK2.1 Flight Control Board for stability and manoeuvrability with a runtime of 90 min. It incorporates a Raspberry Pi camera for real-time video streaming, enabling efficient identification of individuals in need of assistance. The GSM module allows contactless communication, ensuring streamlined and safe interaction. A motor controls the lid of the customizable first aid kit box, facilitating efficient aid delivery. The Neo-6 M GPS module provides accurate localization of the drone and individuals in distress with a horizontal position accuracy of 2.5 m. The UAV collects temperature and humidity data using the DHT 11 sensor having +/- 2 degreesC and +- 5% accuracy respectively. This sensor employs advanced deep learning models, including artificial neural networks (ANN) and generative adversarial networks (GANs), for real-time forest fire prediction with an accuracy of 90.7 % The integration of GANs enhances accuracy through synthetic data generation. Moreover, all these components are interfaced using a Raspberry Pi4 and a GUI, providing a smooth user control experience and end-to-end information for quick and effective emergency response.http://www.sciencedirect.com/science/article/pii/S246806722300086XUAVDeep learningGANsForest fire prediction |
| spellingShingle | Tejas Rathod Vinay Patil R. Harikrishnan Priti Shahane Multipurpose deep learning-powered UAV for forest fire prevention and emergency response HardwareX UAV Deep learning GANs Forest fire prediction |
| title | Multipurpose deep learning-powered UAV for forest fire prevention and emergency response |
| title_full | Multipurpose deep learning-powered UAV for forest fire prevention and emergency response |
| title_fullStr | Multipurpose deep learning-powered UAV for forest fire prevention and emergency response |
| title_full_unstemmed | Multipurpose deep learning-powered UAV for forest fire prevention and emergency response |
| title_short | Multipurpose deep learning-powered UAV for forest fire prevention and emergency response |
| title_sort | multipurpose deep learning powered uav for forest fire prevention and emergency response |
| topic | UAV Deep learning GANs Forest fire prediction |
| url | http://www.sciencedirect.com/science/article/pii/S246806722300086X |
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