{"title":"基于毫米波成像的防水涂层混凝土表面无损裂缝检测与分类","authors":"A. Hirata, Makoto Nakasizuka, Keiichi Sudo","doi":"10.23919/EURAD.2018.8546602","DOIUrl":null,"url":null,"abstract":"This paper presents non-destructive millimeter-wave (MMW) imaging of sub-millimeter wide cracks on concrete surface that are covered by waterproof coating. Measurement of near-field scattering at cracks enables the detection of 0.3-mm-wide cracks by using 76.5-GHz MMW signal whose wavelength is about 3.9 mm. However, the roughness of the waterproof coating also causes near-field scattering, which makes it difficult to identify the cracks and surface roughness of waterproof coating. In order to increase the accuracy of crack detection, we simulated the reflected MMW signals at concrete cracks and surface roughness of waterproof coating, and applied a learning based classifier to these simulation results in order to classify concrete cracks and surface roughness of waterproof coating in the MMW images, and the classification accuracy of 97.5 % was achieved.","PeriodicalId":171460,"journal":{"name":"2018 15th European Radar Conference (EuRAD)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Non-destructive Crack Detection and Classification for Waterproof-coated Concrete Surfaces by Millimeter-wave Imaging\",\"authors\":\"A. Hirata, Makoto Nakasizuka, Keiichi Sudo\",\"doi\":\"10.23919/EURAD.2018.8546602\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents non-destructive millimeter-wave (MMW) imaging of sub-millimeter wide cracks on concrete surface that are covered by waterproof coating. Measurement of near-field scattering at cracks enables the detection of 0.3-mm-wide cracks by using 76.5-GHz MMW signal whose wavelength is about 3.9 mm. However, the roughness of the waterproof coating also causes near-field scattering, which makes it difficult to identify the cracks and surface roughness of waterproof coating. In order to increase the accuracy of crack detection, we simulated the reflected MMW signals at concrete cracks and surface roughness of waterproof coating, and applied a learning based classifier to these simulation results in order to classify concrete cracks and surface roughness of waterproof coating in the MMW images, and the classification accuracy of 97.5 % was achieved.\",\"PeriodicalId\":171460,\"journal\":{\"name\":\"2018 15th European Radar Conference (EuRAD)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 15th European Radar Conference (EuRAD)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/EURAD.2018.8546602\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 15th European Radar Conference (EuRAD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/EURAD.2018.8546602","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Non-destructive Crack Detection and Classification for Waterproof-coated Concrete Surfaces by Millimeter-wave Imaging
This paper presents non-destructive millimeter-wave (MMW) imaging of sub-millimeter wide cracks on concrete surface that are covered by waterproof coating. Measurement of near-field scattering at cracks enables the detection of 0.3-mm-wide cracks by using 76.5-GHz MMW signal whose wavelength is about 3.9 mm. However, the roughness of the waterproof coating also causes near-field scattering, which makes it difficult to identify the cracks and surface roughness of waterproof coating. In order to increase the accuracy of crack detection, we simulated the reflected MMW signals at concrete cracks and surface roughness of waterproof coating, and applied a learning based classifier to these simulation results in order to classify concrete cracks and surface roughness of waterproof coating in the MMW images, and the classification accuracy of 97.5 % was achieved.