Traffic Sign Recognition System (TSRS)
| dc.contributor.author | Hasan, Nazmul | |
| dc.contributor.author | Anzum, Tanvir | |
| dc.date.accessioned | 2020-11-29T04:25:20Z | |
| dc.date.available | 2020-11-29T04:25:20Z | |
| dc.date.issued | 2020-07-18 | |
| dc.description.abstract | TSRS (Traffic Sign Recognition System) may play a significant role in a self-driving car, artificial driver assistances, traffic surveillance as well as traffic safety. Traffic sign recognition is necessary to overcome traffic-related difficulties. The traffic sign recognition system consists of two parts- localization and recognition. In the localization part, where traffic sign region is located and identified by creating a rectangular area. After that, in the recognition part, the rectangular box provided the result for which traffic sign is located in that particular region. In this paper, we describe an approach towards traffic signs recognition system. Here, we worked on 12 selected signs for traffic sign detection and recognition purpose. In this intention, we used Support Vector Machine (SVM) and Convolutional Neural Network (CNN) individually to detect and recognize the traffic signs. We obtained 98.33% accuracy for SVM with an 80:20 train and test data ratio. On the other hand, the test result was 96.40% accurate for CNN. | |
| dc.identifier.other | http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/5207 | |
| dc.identifier.uri | http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/5207 | |
| dc.language.iso | en_US | |
| dc.publisher | Daffodil International University | |
| dc.source | DIU Institutional Repository | |
| dc.subject | Traffic Sign | |
| dc.subject | Neural Networks | |
| dc.title | Traffic Sign Recognition System (TSRS) | |
| dc.type | Other |
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