J. Makkonen, L. A. Marsh, J. Vihonen, M. O’Toole, D. Armitage, Ari Jarvi, A. Peyton, A. Visa
{"title":"Determination of material and geometric properties of metallic objects using the magnetic polarisability tensor","authors":"J. Makkonen, L. A. Marsh, J. Vihonen, M. O’Toole, D. Armitage, Ari Jarvi, A. Peyton, A. Visa","doi":"10.1109/SAS.2015.7133641","DOIUrl":null,"url":null,"abstract":"A walk-through metal detector system has been used for measuring the magnetic polarisability tensor for a variety of metallic objects. We propose a method for classifying objects by their metallic composition using features of the tensor. Furthermore, we investigate the potential of using the tensor representation as an indication geometric properties of the object. The method used is shown to be accurate for classification of material composition. Furthermore, the results suggest that it is possible to use the tensor to distinguish between similar objects of different sizes in limited scenarios. These findings demonstrate the potential for this method, but also suggest the need for further studies.","PeriodicalId":384041,"journal":{"name":"2015 IEEE Sensors Applications Symposium (SAS)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE Sensors Applications Symposium (SAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SAS.2015.7133641","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
A walk-through metal detector system has been used for measuring the magnetic polarisability tensor for a variety of metallic objects. We propose a method for classifying objects by their metallic composition using features of the tensor. Furthermore, we investigate the potential of using the tensor representation as an indication geometric properties of the object. The method used is shown to be accurate for classification of material composition. Furthermore, the results suggest that it is possible to use the tensor to distinguish between similar objects of different sizes in limited scenarios. These findings demonstrate the potential for this method, but also suggest the need for further studies.