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Smart Traffic Management for Emergency Vehicles Using YOLOv8 Algorithm
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Author(s): K. N. V. Satyanarayana (SRKR Engineering College, India), Rongali Gnana Prasanna (SRKR Engineering College, India), Ramineedi Rama Krishna Sai Satwik (SRKR Engineering College, India), Ponala Rupchand (SRKR Engineering College, India), Pigilam Srihaas (SRKR Engineering College, India)and S. Bhuvanapriya (Dhaanish Ahmed College of Engineering, India)
Copyright: 2025
Pages: 20
Source title:
Multidisciplinary Approaches to AI, Data, and Innovation for a Smarter World
Source Author(s)/Editor(s): Sonia Singh (Toss Global Management, UK), Slim Hadoussa (Brest Business School, France), Thangaraja Arumugam (Vellore Institute of Technology, Chennai, India)and S. Suman Rajest (Dhaanish Ahmed College of Engineering, India)
DOI: 10.4018/979-8-3693-9375-8.ch029
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Abstract
Public safety and emergency response depend on rapid and accurate identification of emergency vehicles, particularly ambulances, in increasingly complicated urban traffic. This study uses deep learning to identify ambulances in congested traffic using cutting-edge object detection models like YOLOv5 and YOLOv8 [YOLO (You Only Look Once) is a family of CNN-based object detection models]. This project focusses on recognising ambulances in complex urban circumstances to provide holistic understanding and effective emergency management techniques in urban landscapes. The authors used YOLOv8, a cutting-edge object identification model with high real-time performance, for robust detection. The model, rigorously trained with a precision rate of 0.762, recall rate of 0.631, and mAP50 of 0.69, accurately identifies ambulances under difficult settings. At the IoU threshold of 0.50, the mAP50 of 0.69 indicates strong average precision.
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