Efficient Non-Compression Auto-Encoder for Driving Noise-based Road Surface Anomaly Detection

TL;DR

This paper introduces a non-compression convolutional auto-encoder that efficiently detects road surface anomalies in real-time, especially under wet conditions, with reduced computational cost and improved accuracy.

Contribution

It presents a novel non-compression auto-encoder architecture that outperforms traditional models in efficiency and detection performance for road anomaly detection.

Findings

Computational cost reduced by up to 25 times.

Anomaly detection accuracy improved by up to 7.72%.

Suitable for real-time road safety applications.

Abstract

Wet weather makes water film over the road and that film causes lower friction between tire and road surface. When a vehicle passes the low-friction road, the accident can occur up to 35% higher frequency than a normal condition road. In order to prevent accidents as above, identifying the road condition in real-time is essential. Thus, we propose a convolutional auto-encoder-based anomaly detection model for taking both less computational resources and achieving higher anomaly detection performance. The proposed model adopts a non-compression method rather than a conventional bottleneck structured auto-encoder. As a result, the computational cost of the neural network is reduced up to 1 over 25 compared to the conventional models and the anomaly detection performance is improved by up to 7.72%. Thus, we conclude the proposed model as a cutting-edge algorithm for real-time anomaly detection.