  Road accident can be triggered by wet road because it decreases skid
resistance. To prevent the road accident, detecting road surface abnomality can
be helpful. 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 convolution operation. Moreover, the
above architecture shows higher anomaly detection performance of published
anomaly detection model via experiments. We conclude that NCAE is a
cutting-edge model for road surface anomaly detection.
