Edge computing for driving safety: evaluating deep learning models for cost-effective sound event detection

This paper addresses road safety concerns by investigating low-cost solutions for sound event detection (SED) tailored to driving scenarios. While advanced technologies like deep learning hold promise for improving road safety, their practical implementation often involves expensive sensors and hardware. Distractions, a major cause of accidents, require effective detection and mitigation. This study concentrates on auditory distractions and utilizes SED with low-cost edge devices to identify and timestamp relevant audio events, providing valuable insights into the driving environment. We evaluate state-of-the-art deep learning models on various edge devices, including the 2023 DCASE baseline with convolutional recurrent neural networks (CRNN) and an adapted YOLO vision model for audio spectrograms. Our analysis spans different hardware options, including single-board computers (SBCs) and desktop equipment, offering guidance on cost-effective hardware selection for in-vehicle SED applications. This research aims to contribute to affordable SED solutions in the context of driving safety, with the ultimate goal of advancing road safety efforts worldwide.