M. Nong, J. Yusof, R. Osman, R. Sidek, Suzila Sabil
{"title":"Motorcycle Plated Recognition Based on FPGA","authors":"M. Nong, J. Yusof, R. Osman, R. Sidek, Suzila Sabil","doi":"10.1109/ICCI51257.2020.9247711","DOIUrl":null,"url":null,"abstract":"An intelligent system was developed to recognize motorcycle plate number for traffic enforcement. The system used FPGA as a platform to recognize the plate image. The recognition system was designed to detect still and moving plate images at different resolutions. A motorcycle was defined as the target object and Sobel Edge Detection Algorithm (SEDA) was used on FPGA platform. The results showed that the system was able to recognize motorcycle’s plate number in still and moving conditions. The percentages of the motorcycle image correct detection were 83.3% and 50% for low and high image resolutions, respectively.","PeriodicalId":194158,"journal":{"name":"2020 International Conference on Computational Intelligence (ICCI)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Conference on Computational Intelligence (ICCI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCI51257.2020.9247711","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
An intelligent system was developed to recognize motorcycle plate number for traffic enforcement. The system used FPGA as a platform to recognize the plate image. The recognition system was designed to detect still and moving plate images at different resolutions. A motorcycle was defined as the target object and Sobel Edge Detection Algorithm (SEDA) was used on FPGA platform. The results showed that the system was able to recognize motorcycle’s plate number in still and moving conditions. The percentages of the motorcycle image correct detection were 83.3% and 50% for low and high image resolutions, respectively.