Traffic sign classification using CNN and detection using faster-RCNN and YOLOV4
Autonomous driving cars are becoming popular everywhere and the need for a robust traffic sign recognition system that ensures safety by recognizing traffic signs accurately and fast is increasing. In this paper, we build a CNN that can classify 43 different traffic signs from the German Traffic Sig...
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-12-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844022030808 |
| _version_ | 1828071362169667584 |
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| author | Njayou Youssouf |
| author_facet | Njayou Youssouf |
| author_sort | Njayou Youssouf |
| collection | DOAJ |
| description | Autonomous driving cars are becoming popular everywhere and the need for a robust traffic sign recognition system that ensures safety by recognizing traffic signs accurately and fast is increasing. In this paper, we build a CNN that can classify 43 different traffic signs from the German Traffic Sign Recognition benchmark dataset. The dataset is made up of 39,186 images for training and 12,630 for testing. Our CNN for classification is light and reached an accuracy of 99.20% with only 0.8 M parameters. It is tested also under severe conditions to prove its generalization ability. We also used Faster R–CNN and YOLOv4 networks to implement a recognition system for traffic signs. The German Traffic Sign Detection benchmark dataset was used. Faster R–CNN obtained a mean average precision (mAP) of 43.26% at 6 Frames Per Second (FPS), which is not suitable for real-time application. YOLOv4 achieved an mAP of 59.88% at 35 FPS, which is the preferred model for real-time traffic sign detection. These mAPs are obtained using Intersect Over Union of 50%. A comparative analysis is also presented between these models. |
| first_indexed | 2024-04-11T00:52:14Z |
| format | Article |
| id | doaj.art-6c7de714a9de436e958016bbe2975783 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-11T00:52:14Z |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-6c7de714a9de436e958016bbe29757832023-01-05T08:37:00ZengElsevierHeliyon2405-84402022-12-01812e11792Traffic sign classification using CNN and detection using faster-RCNN and YOLOV4Njayou Youssouf0Corresponding author.; Department of Computer Science and Engineering, Islamic University of Technology, Gazipur 1704, BangladeshAutonomous driving cars are becoming popular everywhere and the need for a robust traffic sign recognition system that ensures safety by recognizing traffic signs accurately and fast is increasing. In this paper, we build a CNN that can classify 43 different traffic signs from the German Traffic Sign Recognition benchmark dataset. The dataset is made up of 39,186 images for training and 12,630 for testing. Our CNN for classification is light and reached an accuracy of 99.20% with only 0.8 M parameters. It is tested also under severe conditions to prove its generalization ability. We also used Faster R–CNN and YOLOv4 networks to implement a recognition system for traffic signs. The German Traffic Sign Detection benchmark dataset was used. Faster R–CNN obtained a mean average precision (mAP) of 43.26% at 6 Frames Per Second (FPS), which is not suitable for real-time application. YOLOv4 achieved an mAP of 59.88% at 35 FPS, which is the preferred model for real-time traffic sign detection. These mAPs are obtained using Intersect Over Union of 50%. A comparative analysis is also presented between these models.http://www.sciencedirect.com/science/article/pii/S2405844022030808Convolutional neural networkObject detectionFaster R–CNNYOLOv4Traffic sign recognitionTraffic sign classification |
| spellingShingle | Njayou Youssouf Traffic sign classification using CNN and detection using faster-RCNN and YOLOV4 Heliyon Convolutional neural network Object detection Faster R–CNN YOLOv4 Traffic sign recognition Traffic sign classification |
| title | Traffic sign classification using CNN and detection using faster-RCNN and YOLOV4 |
| title_full | Traffic sign classification using CNN and detection using faster-RCNN and YOLOV4 |
| title_fullStr | Traffic sign classification using CNN and detection using faster-RCNN and YOLOV4 |
| title_full_unstemmed | Traffic sign classification using CNN and detection using faster-RCNN and YOLOV4 |
| title_short | Traffic sign classification using CNN and detection using faster-RCNN and YOLOV4 |
| title_sort | traffic sign classification using cnn and detection using faster rcnn and yolov4 |
| topic | Convolutional neural network Object detection Faster R–CNN YOLOv4 Traffic sign recognition Traffic sign classification |
| url | http://www.sciencedirect.com/science/article/pii/S2405844022030808 |
| work_keys_str_mv | AT njayouyoussouf trafficsignclassificationusingcnnanddetectionusingfasterrcnnandyolov4 |