Non-Compression Auto-Encoder for Detecting Road Surface Abnormality via Vehicle Driving Noise
This work addresses road safety by detecting wet or abnormal surfaces for drivers and transportation systems, representing an incremental improvement in anomaly detection methods.
The paper tackled road surface anomaly detection to prevent accidents by proposing a non-compression auto-encoder (NCAE) that uses vehicle driving noise, achieving a 4.20% higher AUROC and 2.99 times faster decision than prior models.
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.