{"title":"基于车辆行驶噪声检测路面异常的无压缩自编码器","authors":"Yeonghyeon Park, JongHee Jung","doi":"10.1109/ICACEH54312.2021.9768853","DOIUrl":null,"url":null,"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.","PeriodicalId":359434,"journal":{"name":"2021 IEEE 3rd International Conference on Architecture, Construction, Environment and Hydraulics (ICACEH)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Non-Compression Auto-Encoder for Detecting Road Surface Abnormality via Vehicle Driving Noise\",\"authors\":\"Yeonghyeon Park, JongHee Jung\",\"doi\":\"10.1109/ICACEH54312.2021.9768853\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":359434,\"journal\":{\"name\":\"2021 IEEE 3rd International Conference on Architecture, Construction, Environment and Hydraulics (ICACEH)\",\"volume\":\"34 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-03-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE 3rd International Conference on Architecture, Construction, Environment and Hydraulics (ICACEH)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICACEH54312.2021.9768853\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE 3rd International Conference on Architecture, Construction, Environment and Hydraulics (ICACEH)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICACEH54312.2021.9768853","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Non-Compression Auto-Encoder for Detecting Road Surface Abnormality via Vehicle Driving Noise
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.