UGC Approved Journal no 63975(19)
New UGC Peer-Reviewed Rules

ISSN: 2349-5162 | ESTD Year : 2014
Volume 13 | Issue 10 | October 2026

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Published in:

Volume 12 Issue 6
June-2025
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2506274


Registration ID:
564394

Page Number

c524-c533

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Title

Autonomous Rescue Boat using AI

Abstract

This paper provides an overview of the development of an Artificial Intelligence (AI) driven Automated Rescue Boat (ARB), capable of performing more reliable and efficient rescue operations for urban flooding events. Each year, drowning is responsible for a significant number of accidental deaths globally, primarily in areas where there is insufficient and immediate access to an effective rescue process, standard lifebuoys and manual rescue processes are constrained by human effort which can lead to delays in rescue responses and potentially an extremely hazardous situation for rescuers undertaking the rescue efforts. To mitigate these issues, we propose the Automated Intelligent Lifesaving Buoy, an autonomous rescue boat hybrid, equipped with a camera that can detect, monitor and facilitate rescue actions to drowning victims in a real time situation. This is the opposite of a conventional rescue operational systems such as lifebuoys that are overly reliant on person visibility, and an operator who will need to remote control and act with precision under pressure . We apply computer vision, object detection and victim's face recognition using Yolov8, Tensor Flow Lite, Py Torch, OpenCV. The ARB is built using a Raspberry Pi including BLDC motors and Electronic Drive Controller (EDC) technology as part of buoy propulsion. The U-Shape design means that a victim will be safely gripped by the buoy, and face detection is based on Local Binary Pattern Histogram (LBPH) which increases certainty of the victim, even in low visibility and inclement conditions. In our tests, the ARB achieved 95% obstacle detection, a 92% identification rate of a drowning victim and successfully completed 15-meter rescues in an average of just 30 seconds using real time analysis

Key Words

Raspberry Pi, High-resolution cameras, AI algorithms, Image processing, Urban flooding.

Cite This Article

"Autonomous Rescue Boat using AI", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 6, page no.c524-c533, June-2025, Available :http://www.jetir.org/papers/JETIR2506274.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Autonomous Rescue Boat using AI", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 6, page no. ppc524-c533, June-2025, Available at : http://www.jetir.org/papers/JETIR2506274.pdf

Publication Details

Published Paper ID: JETIR2506274
Registration ID: 564394
Published In: Volume 12 | Issue 6 | Year June-2025
DOI (Digital Object Identifier): https://doi.org/10.56975/jetir.v12i6.564394
Page No: c524-c533
Country: Pune, Maharashtra, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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