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

Yeonghyeon Park, JongHee Jung
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引用次数: 2

Abstract

Road accidents can be triggered by wet roads because it decreases skid resistance. To prevent road accidents, detecting abnormal road surfaces 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 models via experiments. We conclude that NCAE is a cutting-edge model for road surface anomaly detection with 4.20% higher AUROC and 2.99 times faster decisions than before.
基于车辆行驶噪声检测路面异常的无压缩自编码器
潮湿的路面会降低防滑阻力,从而引发交通事故。为了防止交通事故,检测异常路面是非常有用的。本文提出了一种基于深度学习的低成本实时异常检测体系结构,该体系结构采用非压缩自编码器(NCAE)命名。该结构可以通过卷积运算反映时间序列信息的正向和反向因果关系。通过实验表明,该体系结构比已发布的异常检测模型具有更高的异常检测性能。NCAE模型的AUROC提高了4.20%,决策速度提高了2.99倍,是路面异常检测的前沿模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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