JongHee Jung

YeongHyeon Park, JongHee Jung

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.

YeongHyeon Park, JongHee Jung

Road accident can be triggered by wet road because it decreases skid resistance. To prevent the road accident, detecting road surface abnomality is highly useful. In this paper, we propose the deep learning based cost-effective real-time anomaly detection architecture, naming with non-compression auto-encoder (NCAE). The proposed architecture can reflect forward and backward causality of time series information via convolutional operation. Moreover, the above architecture shows higher anomaly detection performance of published anomaly detection model via experiments. We conclude that NCAE as a cutting-edge model for road surface anomaly detection with 4.20\% higher AUROC and 2.99 times faster decision than before.