Efficient Non‐Compression Auto‐Encoder for Driving Noise‐Based Road Surface Anomaly Detection
YeongHyeon Park, JongHee Jung · IEEJ Transactions on Electrical and Electronic Engineering · 2022
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 with 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. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.