基于图像的卷积神经网络路面类型分类

2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES) Pub Date : 2020-07-01 DOI:10.1109/INES49302.2020.9147199

A. Riid, Davide L. Manna, S. Astapov

{"title":"基于图像的卷积神经网络路面类型分类","authors":"A. Riid, Davide L. Manna, S. Astapov","doi":"10.1109/INES49302.2020.9147199","DOIUrl":null,"url":null,"abstract":"Road pavement type classification is important in route planning, road maintenance and for autonomous vehicles. In this paper, we propose a deep learning based method for automatic road type classification from road surface images. The resulting binary classifiers (paved and non-paved road classes) achieve up to 98% classification accuracy on the test set that contains over 100 000 real-world road images that cover a distance of over 300 km.","PeriodicalId":175830,"journal":{"name":"2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Image-Based Pavement Type Classification with Convolutional Neural Networks\",\"authors\":\"A. Riid, Davide L. Manna, S. Astapov\",\"doi\":\"10.1109/INES49302.2020.9147199\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Road pavement type classification is important in route planning, road maintenance and for autonomous vehicles. In this paper, we propose a deep learning based method for automatic road type classification from road surface images. The resulting binary classifiers (paved and non-paved road classes) achieve up to 98% classification accuracy on the test set that contains over 100 000 real-world road images that cover a distance of over 300 km.\",\"PeriodicalId\":175830,\"journal\":{\"name\":\"2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES)\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INES49302.2020.9147199\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INES49302.2020.9147199","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 4

摘要

道路路面类型分类在路线规划、道路维护和自动驾驶汽车中具有重要意义。本文提出了一种基于深度学习的路面图像道路类型自动分类方法。所得到的二元分类器(铺砌和非铺砌道路类别)在包含超过100,000张真实道路图像的测试集上实现了高达98%的分类准确率，这些图像覆盖的距离超过300公里。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Image-Based Pavement Type Classification with Convolutional Neural Networks

Road pavement type classification is important in route planning, road maintenance and for autonomous vehicles. In this paper, we propose a deep learning based method for automatic road type classification from road surface images. The resulting binary classifiers (paved and non-paved road classes) achieve up to 98% classification accuracy on the test set that contains over 100 000 real-world road images that cover a distance of over 300 km.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES)

自引率

0.00%

发文量