Deep Learning Based Traffic Sign Recognition Using YOLO Algorithm
Traffic sign detection has attracted a lot of attention in recent years among object recognition applications. Accurate and fast detection of traffic signs will also eliminate an important technical problem in autonomous vehicles. With the developing artificial intelligency technology, deep learning...
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| Format: | Article |
| Language: | English |
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Düzce University
2024-01-01
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| Series: | Düzce Üniversitesi Bilim ve Teknoloji Dergisi |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/tr/download/article-file/2814068 |
| _version_ | 1797300853716025344 |
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| author | Gökalp Çınarer |
| author_facet | Gökalp Çınarer |
| author_sort | Gökalp Çınarer |
| collection | DOAJ |
| description | Traffic sign detection has attracted a lot of attention in recent years among object recognition applications. Accurate and fast detection of traffic signs will also eliminate an important technical problem in autonomous vehicles. With the developing artificial intelligency technology, deep learning applications can distinguish objects with high perception and accurate detection. New applications are being tested in this area for the detection of traffic signs using artificial intelligence technology. In this context, this article has an important place in correctly detecting traffic signs with deep learning algorithms. In this study, three model of (You Only Look Once) YOLOv5, an up-to-date algorithm for detecting traffic signs, were used. A system that uses deep learning models to detect traffic signs is proposed. In the proposed study, real-time plate detection was also performed. When the precision, recall and mAP50 values of the models were compared, the highest results were obtained as 99.3, 95% and 98.1%, respectively. Experimental results have supported that YOLOv5 architectures are an accurate method for object detection with both image and video. It has been seen that YOLOv5 algorithms are quite successful in detecting traffic signs and average precession. |
| first_indexed | 2024-03-07T23:13:59Z |
| format | Article |
| id | doaj.art-6ae15c2f28794e08bb9012195dab44fc |
| institution | Directory Open Access Journal |
| issn | 2148-2446 |
| language | English |
| last_indexed | 2024-03-07T23:13:59Z |
| publishDate | 2024-01-01 |
| publisher | Düzce University |
| record_format | Article |
| series | Düzce Üniversitesi Bilim ve Teknoloji Dergisi |
| spelling | doaj.art-6ae15c2f28794e08bb9012195dab44fc2024-02-21T14:07:27ZengDüzce UniversityDüzce Üniversitesi Bilim ve Teknoloji Dergisi2148-24462024-01-0112121922910.29130/dubited.121490197Deep Learning Based Traffic Sign Recognition Using YOLO AlgorithmGökalp Çınarer0BOZOK ÜNİVERSİTESİTraffic sign detection has attracted a lot of attention in recent years among object recognition applications. Accurate and fast detection of traffic signs will also eliminate an important technical problem in autonomous vehicles. With the developing artificial intelligency technology, deep learning applications can distinguish objects with high perception and accurate detection. New applications are being tested in this area for the detection of traffic signs using artificial intelligence technology. In this context, this article has an important place in correctly detecting traffic signs with deep learning algorithms. In this study, three model of (You Only Look Once) YOLOv5, an up-to-date algorithm for detecting traffic signs, were used. A system that uses deep learning models to detect traffic signs is proposed. In the proposed study, real-time plate detection was also performed. When the precision, recall and mAP50 values of the models were compared, the highest results were obtained as 99.3, 95% and 98.1%, respectively. Experimental results have supported that YOLOv5 architectures are an accurate method for object detection with both image and video. It has been seen that YOLOv5 algorithms are quite successful in detecting traffic signs and average precession.https://dergipark.org.tr/tr/download/article-file/2814068deep learningtraffic sign recognitionyoloderin öğrenmetrafik işareti tanımayolodeep learningtraffic sign recognition |
| spellingShingle | Gökalp Çınarer Deep Learning Based Traffic Sign Recognition Using YOLO Algorithm Düzce Üniversitesi Bilim ve Teknoloji Dergisi deep learning traffic sign recognition yolo derin öğrenme trafik işareti tanıma yolo deep learning traffic sign recognition |
| title | Deep Learning Based Traffic Sign Recognition Using YOLO Algorithm |
| title_full | Deep Learning Based Traffic Sign Recognition Using YOLO Algorithm |
| title_fullStr | Deep Learning Based Traffic Sign Recognition Using YOLO Algorithm |
| title_full_unstemmed | Deep Learning Based Traffic Sign Recognition Using YOLO Algorithm |
| title_short | Deep Learning Based Traffic Sign Recognition Using YOLO Algorithm |
| title_sort | deep learning based traffic sign recognition using yolo algorithm |
| topic | deep learning traffic sign recognition yolo derin öğrenme trafik işareti tanıma yolo deep learning traffic sign recognition |
| url | https://dergipark.org.tr/tr/download/article-file/2814068 |
| work_keys_str_mv | AT gokalpcınarer deeplearningbasedtrafficsignrecognitionusingyoloalgorithm |