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Detecting drowning accidents accurately and immediately is essential to saving lives because drowning is a major cause of unintentional death worldwide. Through the use of You Only Look Once (YOLO) and video and image input, we suggest a method in this research for the detection of multiple drowning persons. A deep learning model called You Only Look Once (YOLO)is suited for processing video data since it is made to recognize temporal connections in sequential data. In order to capture the area above the water surface, our suggested system employs video data taken from security cameras placed close to water sources. For the YOLO model to effectively identify the presence of numerous drowning victims, the video data is per-processed. The results show that the suggested approach is effective at reliably detecting numerous drowning persons and trigger an alarm to alert the lifeguards to save the drowning persons. We tested our proposed system using a date set of simulated drowning incidents. Our suggested technique has the potential to be applied in practical situations to enhance the speed and precision of drowning detection, which could save many lives.
Keywords:
Drowning Detection, You Only Look Once (YOLO), alarm
Cite Article:
"Drowning Detection System", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 5, page no.747 - 751, May-2023, Available :http://www.ijrti.org/papers/IJRTI2305117.pdf
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000205212
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator