False Positive Reduction in Emergency Vehicle Detection Using a Multimodal Edge-Based System - Kristiawan, Indra; Galinium, Maulahikmah; Dzulhijjah, Dwi Ahmad; Kusrini; Dewantara, Bima Sena Bayu; Nasution, Henry (Engineering, Technology & Applied Science Research, 2026)
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.