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A Deep Learning Approach for Traffic Sign Detection and Recognition Using Improved YOLOV5s

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dc.contributor.author Hossain, MD. Ariful
dc.contributor.author Hossain, Anwar
dc.date.accessioned 2023-04-05T08:24:38Z
dc.date.available 2023-04-05T08:24:38Z
dc.date.issued 23-01-29
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/10151
dc.description.abstract Traffic sign detection is one of the most challenging tasks for autonomous vehicles, especially for the detection of different types of signs and real-time applications. Unmanned driving systems face a lot of problems to recognize traffic signs faster and more accurately. In this paper, we propose a model using improved YOLOv5 to detect Traffic signs and recognize them properly. Our dataset consists of 3500 pictures of the traffic sign and we have annotated all that pictures in YOLOv5 format. There is 39 classification of our dataset on all pictures based on the traffic sign. This system can be used for unmanned driving vehicles. Using this model a device can make which will help drivers who are driving a car. After the implementation of our dataset with the help of improved YOLOv5, the output shows an accuracy of 86.75% in different conditions such as low light, cloudy, rainy, and sunny. en_US
dc.language.iso en_US en_US
dc.publisher Daffodil International University en_US
dc.subject Traffic control en_US
dc.subject Autonomous vehicles en_US
dc.subject Traffic signs en_US
dc.subject Driving vehicles en_US
dc.title A Deep Learning Approach for Traffic Sign Detection and Recognition Using Improved YOLOV5s en_US
dc.type Other en_US


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