  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.
