Authors : Deivamani.G; Govindhraj.P; Jagadeesh.S; Mohammed Asik.A; Sudarsan.T

Volume/Issue : Volume 9 - 2024, Issue 5 - May

Google Scholar : https://tinyurl.com/3j4sr4w2

Scribd : https://tinyurl.com/286dntcj

DOI : https://doi.org/10.38124/ijisrt/IJISRT24MAY277

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Drowning people in India approximately around 38000 peoples per year leads to dead finally because of, we have insufficient water rescue or timely emergency response to search and rescue team during emergency, also the lack of information to the rescue team about the drowning people place. We should believe that a few seconds' difference could have saved a person’s life. The timely information conveyed to the rescue team is also an important criterion for drowning to dead rate being very higher At first, we make a dataset, which contains many human targets at sea. Then, we improve the algorithm In the feature extraction network, we use the residual module with channel attention mechanism. Finally, on the settings of the raspberry pi Pico with GPS and GSM, we use a linear transformation method to deal with the python generated by clustering algorithm. The detection accuracy of the improved algorithm for human targets at sea is improved, which has a good detection effect. The drone with detecting and alerting with voice message to the Rescue Team at remote end with required all details about the drowning people make sense for faster rescue and save as the highest accuracy. The camera detection of the rescue Drone had a proper in that the range of the active camera and the speed of the video with Wi-Fi to the control room also optimal for the detection to work properly.

Keywords : ESP8266, ANN, Arduino Uno, Python Software, GSM/GPS Module.

References :

1. Gonçalves, J.; Henriques, R. UAV photogrammetry for topographic monitoring of coastal areas. ISPRS J. Photogramm. Remote. Sens. 2015, 104, 101–111. [Google Scholar] [CrossRef]
2. Rokhmana, C.A. The potential of UAV-based remote sensing for supporting precision agriculture in Indonesia. Procedia Environ. Sci. 2015, 24, 245–253. [Google Scholar] [CrossRef] [Green Version]
3. Torresan, C.; Berton, A.; Carotenuto, F.; Di Gennaro, S.F.; Gioli, B.; Matese, A.; Miglietta, F.; Vagnoli, C.; Zaldei, A.; Wallace, L. Forestry applications of UAVs in Europe: A review. Int. J. Remote. Sens. 2016, 38, 2427–2447. [Google Scholar] [CrossRef]
4. Min, B.C.; Cho, C.H.; Choi, K.M.; Kim, D.H. Development of a micro quad-rotor UAV for monitoring an indoor environment. Adv. Robot. 2009, 6, 262–271. [Google Scholar] [CrossRef]
5. Samiappan, S.; Turnage, G.; Hathcock, L.; Casagrande, L.; Stinson, P.; Moorhead, R. Using unmanned aerial vehicles for high-resolution remote sensing to map invasive Phragmitesaustralis in coastal wetlands. Int. J. Remote. Sens. 2017, 38, 2199–2217. [Google Scholar] [CrossRef]
6. Erdelj, M.; Natalizio, E.; Chowdhury, K.R.; Akyildiz, I.F. Help from the sky: Leveraging UAVs for disaster management. IEEE Pervasive Comput. 2016, 16, 24–32. [Google Scholar] [CrossRef]
7. Al-Kaff, A.; Gómez-Silva, M.J.; Moreno, F.M.; De La Escalera, A.; Armingol, J.M. An appearance-based tracking algorithm for aerial search and rescue purposes. Sensors 2019, 19, 652. [Google Scholar] [CrossRef] [Green Version]
8. Lu, Y.; Xue, Z.; Xia, G.-S.; Zhang, L. A survey on vision-based UAV navigation. Geospat. Inf. Sci. 2018, 21, 21–32. [Google Scholar] [CrossRef] [Green Version]
9. Liu, Y.; Noguchi, N.; Liang, L. Development of a positioning system using UAV-based computer vision for an airboat navigation in paddy field. Comput. Electron. Agric. 2019, 162, 126–133. [Google Scholar] [CrossRef]
10. Apolo-Apolo, O.; Martínez-Guanter, J.; Egea, G.; Raja, P.; Pérez-Ruiz, M. Deep learning techniques for estimation of the yield and size of citrus fruits using a UAV. Eur. J. Agron. 2020, 115, 126030. [Google Scholar] [CrossRef]
11. Aguilar, W.G.; Luna, M.A.; Moya, J.F.; Abad, V.; Parra, H.; Ruiz, H. Pedestrian detection for UAVs using cascade classifiers with meanshift. In Proceedings of the 2017 IEEE 11th International Conference on Semantic Computing (ICSC), San Diego, CA, USA, 30 January–1 February 2017; pp. 509–514. [Google Scholar] [CrossRef]
12. Hu, B.; Wang, J. Deep learning based hand gesture recognition and UAV flight controls. Int. J. Autom. Comput. 2019, 17, 17–29. [Google Scholar] [CrossRef]
13. Alotaibi, E.T.; Alqefari, S.S.; Koubaa, A. LSAR: Multi-UAV collaboration for search and rescue missions. IEEE Access 2019, 7, 55817–55832. [Google Scholar] [CrossRef]
14. Lygouras, E.; Santavas, N.; Taitzoglou, A.; Tarchanidis, K.; Mitropoulos, A.; Gasteratos, A. Unsupervised human detection with an embedded vision system on a fully autonomous UAV for search and rescue operations. Sensors 2019, 19, 3542. [Google Scholar] [CrossRef] [PubMed] [Green Version]
15. Sudhakar, S.; Vijayakumar, V.; Kumar, C.S.; Priya, V.; Ravi, L.; Subramaniyaswamy, V. Unmanned aerial vehicle (UAV) based forest fire detection and monitoring for reducing false alarms in forest-fires. Comput. Commun. 2020, 149, 1–16. [Google Scholar] [CrossRef]