Detection Of Human Life In Fire Scenarios Using YOLOv8

Author(s)	Safia Rafi, Shilpa Nagaraj, Nikith Murali, Sohan S, Mohammed Fardeen Shariff, Santhosh Kumar K L
Country	India
Abstract	This system is a human detection system that has been designed for fire rescue operations using most advanced deep learning and computer vision techniques in dealing with smoke vacuum visibility and structure obstructions issues. The real-time object detection is done by YOLOv8 within OpenCV and other technologies for image acquisition and video enhancement such as CLAHE and super-resolution. It detects and highlights users inside the live video feed by surrounding them with green bounding boxes to facilitate prompt localization. Another purpose of this system is posture detection, which classifies the postures of lying, standing, or crouching to aid in prioritizing individuals. The modular feature of this technology allows it to easily adapt to different environments and be compatible with systems such as drone-mounted or stationary cameras. Hence this technology is robust, scalable, and suitable for real-time applications in rescue missions.
Keywords	Human Detection, YOLOv8, Fire Detection, Posture Detection
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 7, Issue 1, January-February 2025
Published On	2025-01-05
DOI	https://doi.org/10.36948/ijfmr.2025.v07i01.34516
Short DOI	https://doi.org/g82hhb

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International Journal For Multidisciplinary Research