{"title":"基于谱聚类的裂纹检测:考虑裂纹特征和连接的自调谐","authors":"Daiki Shiotsuka, Kousuke Matsushima, Osamu Takahashi","doi":"10.1109/MoRSE48060.2019.8998708","DOIUrl":null,"url":null,"abstract":"Cracks on the pavement road cause various traffic problems. Hence, we should repair them properly. Nowadays, there are a variety of crack detection method based on computer vision for operation efficiency. Spectral Clustering is one of them and effective. However, detection accuracy may decrease depending on the image because roads often have many bumps. In this paper, we cluster images having less noise into two clusters, while noisy images into three clusters. The number of clusters is determined automatically by considering the relationship between crack feature and the number of connections.","PeriodicalId":111606,"journal":{"name":"2019 International Conference on Mechatronics, Robotics and Systems Engineering (MoRSE)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Crack Detection using Spectral Clustering: Self-Tuning Considering Crack Feature and Connections\",\"authors\":\"Daiki Shiotsuka, Kousuke Matsushima, Osamu Takahashi\",\"doi\":\"10.1109/MoRSE48060.2019.8998708\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Cracks on the pavement road cause various traffic problems. Hence, we should repair them properly. Nowadays, there are a variety of crack detection method based on computer vision for operation efficiency. Spectral Clustering is one of them and effective. However, detection accuracy may decrease depending on the image because roads often have many bumps. In this paper, we cluster images having less noise into two clusters, while noisy images into three clusters. The number of clusters is determined automatically by considering the relationship between crack feature and the number of connections.\",\"PeriodicalId\":111606,\"journal\":{\"name\":\"2019 International Conference on Mechatronics, Robotics and Systems Engineering (MoRSE)\",\"volume\":\"118 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 International Conference on Mechatronics, Robotics and Systems Engineering (MoRSE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MoRSE48060.2019.8998708\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Mechatronics, Robotics and Systems Engineering (MoRSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MoRSE48060.2019.8998708","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Crack Detection using Spectral Clustering: Self-Tuning Considering Crack Feature and Connections
Cracks on the pavement road cause various traffic problems. Hence, we should repair them properly. Nowadays, there are a variety of crack detection method based on computer vision for operation efficiency. Spectral Clustering is one of them and effective. However, detection accuracy may decrease depending on the image because roads often have many bumps. In this paper, we cluster images having less noise into two clusters, while noisy images into three clusters. The number of clusters is determined automatically by considering the relationship between crack feature and the number of connections.