Deep Convolutional Neural Network for Roadway Incident Surveillance Using Audio Data

TL;DR

This paper presents a deep CNN approach utilizing audio data, including MFCC and spectrogram features, to accurately classify roadway events like crashes, hazards, and sirens, achieving up to 94% accuracy for enhanced traffic safety monitoring.

Contribution

It introduces a novel audio-based crash detection system using CNNs and data augmentation, expanding traffic incident surveillance beyond traditional video and sensor methods.

Findings

Achieved up to 94% classification accuracy.

Effective use of audio augmentation techniques.

Identified key audio features for event classification.

Abstract

Crash events identification and prediction plays a vital role in understanding safety conditions for transportation systems. While existing systems use traffic parameters correlated with crash data to classify and train these models, we propose the use of a novel sensory unit that can also accurately identify crash events: microphone. Audio events can be collected and analyzed to classify events such as crash. In this paper, we have demonstrated the use of a deep Convolutional Neural Network (CNN) for road event classification. Important audio parameters such as Mel Frequency Cepstral Coefficients (MFCC), log Mel-filterbank energy spectrum and Fourier Spectrum were used as feature set. Additionally, the dataset was augmented with more sample data by the use of audio augmentation techniques such as time and pitch shifting. Together with the feature extraction this data augmentation can achieve reasonable accuracy. Four events such as crash, tire skid, horn and siren sounds can be accurately identified giving indication of a road hazard that can be useful for traffic operators or paramedics. The proposed methodology can reach accuracy up to 94%. Such audio systems can be implemented as a part of an Internet of Things (IoT) platform that can complement video-based sensors without complete coverage.