False Positive Reduction in Emergency Vehicle Detection Using a Multimodal Edge-Based System

Indra Kristiawan; Maulahikmah Galinium; Dwi Ahmad Dzulhijjah; Kusrini; Bima Sena Bayu Dewantara; Henry Nasution

doi:10.48084/etasr.13619

Authors

Indra Kristiawan Master of Mechanical Engineering Study Program, Swiss German University, Tangerang, Indonesia | Divisi Kecerdasan Buatan, PT. Piranti Kecerdasan Buatan, Tangerang, Indonesia
Maulahikmah Galinium Information Technology Department, Swiss German University Tangerang, Indonesia
Dwi Ahmad Dzulhijjah Department of Cyber Physical System, Politeknik Elektronika Negeri Surabaya, Surabaya, Indonesia | Divisi Kecerdasan Buatan, PT. Piranti Kecerdasan Buatan, Tangerang, Indonesia
Kusrini Informatics Postgraduate Program, Universitas AMIKOM Yogyakarta, Indonesia | Fundacion para la Investigacion y Desarrollo Tecnologico de la Sociedad del Conocimiento, Murcia, Spain
Bima Sena Bayu Dewantara Department of Cyber Physical Systems, Politeknik Elektronika Negeri Surabaya, Surabaya, Indonesia
Henry Nasution Swiss German University, Tangerang Indonesia | Teknologi Rekayasa Energi Terbarukan, Fakultas Teknologi Industri, Universitas Bung Hatta, Padang, Indonesia

Volume: 16 | Issue: 2 | Pages: 33947-33953 | April 2026 | https://doi.org/10.48084/etasr.13619

Received: 24 July 2025 | Revised: 9 September 2025 | Accepted: 15 September 2025 | Online: 4 April 2026

Corresponding author: Henry Nasution

Abstract

Traditional vision-based emergency vehicle detection systems used in Adaptive Traffic Signal Control (ATSC) suffer from high False Positive (FP) classifications that compromise traffic flow efficiency and system reliability. This study proposes a multimodal detection framework that integrates a YOLOv5 visual detector with an ESP32-S3 acoustic analyzer using Mel-Filterbank Energy (MFE) features, with dual-modality confirmation achieved through AND-gate fusion. Field evaluation conducted over 11 days and comprising 1,380 detection events demonstrated a complete FP elimination from 900 to 0 cases, while preserving detection capability and achieving a precision of 100%, a specificity of 100%, a recall of 87.1%, a standard accuracy of 99.4%, and a preemption rate of 87.1%, corresponding to a 55.3 percentage point/% improvement over the vision-only baseline. These results confirm the effectiveness of selective acoustic confirmation in reducing detection ambiguities, maintaining real-time responsiveness, and enhancing the robustness of emergency vehicle detection in urban traffic management systems.

Keywords:

Adaptive Traffic Signal Control (ATSC), computer vision, audio detection, emergency vehicle detection, sustainable cities and communities

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