Real-Time Car Honk Detection for On-the-Go Application using Lightweight Deep Learning Model

Noise pollution from vehicular honking is a pressing issue in urban environments, necessitating the development of efficient honk detection systems. While deep learning models have shown promise in detecting car honks, their high computational demands limit their deployment on mobile devices. This research introduces a novel approach to car honk detection, introducing a lightweight deep learning models optimized for mobile devices. We developed a mobile application that detects vehicular honks in real-time, offering a practical solution to monitor noise pollution and enhance pedestrian safety. Our proposed model achieved a 97.65% accuracy with low average inference time and latency of 63.8 ms and 288.79 ms, respectively. This application can alert pedestrians wearing earphones or headphones about nearby honks, helping prevent accidents. Our work addresses key challenges in resource optimization, real-time processing, and latency reduction, providing a significant contribution to urban safety and environmental monitoring.