# Non-Compression Auto-Encoder for Detecting Road Surface Abnormality via Vehicle Driving Noise

arXiv: https://arxiv.org/abs/2103.12992 · HTML: https://mlsanity.com/paper/2103.12992/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 10 references that are in the map.
- First posted on arXiv 24 Mar 2021.

## Builds on (most cited first, 1 of 1)

- [Anomaly Detection in Particulate Matter Sensor using Hypothesis Pruning Generative Adversarial Network](https://mlsanity.com/paper/1912.00583/) (2019)

## Built on by (most cited first, 2 of 2)

- [Efficient Non-Compression Auto-Encoder for Driving Noise-based Road Surface Anomaly Detection](https://mlsanity.com/paper/2111.10985/) (2021)
- [Noise Reduction and Driving Event Extraction Method for Performance Improvement on Driving Noise-based Surface Anomaly Detection](https://mlsanity.com/paper/2112.07214/) (2021)

## Abstract (arXiv)

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.