Nur Nabilah Abu Mangshor, Norzihani Saharuddin, Shafaf Ibrahim, A. Fadzil, K. A. Samah
{"title":"A Real-Time Speed Limit Sign Recognition System for Autonomous Vehicle Using SSD Algorithm","authors":"Nur Nabilah Abu Mangshor, Norzihani Saharuddin, Shafaf Ibrahim, A. Fadzil, K. A. Samah","doi":"10.1109/ICCSCE52189.2021.9530923","DOIUrl":null,"url":null,"abstract":"In today’s technology-driven world, there are a lot of devices and products have been invented including the Autonomous Vehicle (AV). Autonomous vehicle is a driverless vehicle which able to drive on its own. It works with the aid of various embedded systems and sensors that help the AV to function greatly. Speed limit signs recognition is one of the TSR essential features for AV where it helps to automatically detect and recognize speed limit signs on the road. There are many speed limit signs available on the road including 30km/h, 60km/h, 90km/h signs and to name a few. However, the interclass similarity among these speed limit signs has created a challenge for the TSR system in detection and recognition process. Therefore, this study proposes image processing technique to develop the speed limit sign recognition for TSR system utilizing a single layer network called Single Shot Multibox Detector (SSD) algorithm. The German Traffic Sign Dataset (GTSD) is used for the purpose of training the model and the model is then tested using the real-time images of standard Malaysian speed limit signs. An accuracy testing using confusion matrix is conducted to find the overall accuracy of the system. A total 100 images are used during testing and the system achieved over 92.4% of the average accuracy for detection and recognition of the speed limit signs.","PeriodicalId":285507,"journal":{"name":"2021 11th IEEE International Conference on Control System, Computing and Engineering (ICCSCE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 11th IEEE International Conference on Control System, Computing and Engineering (ICCSCE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCSCE52189.2021.9530923","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
In today’s technology-driven world, there are a lot of devices and products have been invented including the Autonomous Vehicle (AV). Autonomous vehicle is a driverless vehicle which able to drive on its own. It works with the aid of various embedded systems and sensors that help the AV to function greatly. Speed limit signs recognition is one of the TSR essential features for AV where it helps to automatically detect and recognize speed limit signs on the road. There are many speed limit signs available on the road including 30km/h, 60km/h, 90km/h signs and to name a few. However, the interclass similarity among these speed limit signs has created a challenge for the TSR system in detection and recognition process. Therefore, this study proposes image processing technique to develop the speed limit sign recognition for TSR system utilizing a single layer network called Single Shot Multibox Detector (SSD) algorithm. The German Traffic Sign Dataset (GTSD) is used for the purpose of training the model and the model is then tested using the real-time images of standard Malaysian speed limit signs. An accuracy testing using confusion matrix is conducted to find the overall accuracy of the system. A total 100 images are used during testing and the system achieved over 92.4% of the average accuracy for detection and recognition of the speed limit signs.