Please use this identifier to cite or link to this item:
https://dspace.ctu.edu.vn/jspui/handle/123456789/73767
Title: | TRAFFIC SIGN RECOGNITION WITH YOLOv5 |
Authors: | Nguyễn, Thanh Hải Võ, Hoàng Nguyễn Vỹ |
Keywords: | CÔNG NGHỆ THÔNG TIN-CHẤT LƯỢNG CAO |
Issue Date: | 2021 |
Publisher: | Trường Đại Học Cần Thơ |
Abstract: | Nowadays, road traffic accidents are increasing dramatically, and one of the most reasons is the lack of knowledge about the traffic signs of the drivers. Traffic sign recognition has long been an important component of driver-assistance systems, as it may help drivers avoid a wide range of possible hazards while also improving their driving experience. On the other hand, traffic sign recognition is a realistic assignment with numerous constraints, like the visual environment, physical damages, and partial occasions. Therefore, it is necessary to have an application that can meet these requirements in order to reduce the number of traffic accidents because of the lack of understanding of traffic signs. In this study, the object detection algorithm - YOLOv5, a unified deep learning architecture for real-time recognition applications, is used for recognizing traffic signs due to its speed and accuracy. The traffic signs dataset used for this study was modified from the Zalo AI Challenge 2020 including 4469 images belonging to 7 different classes. The result shows that the model has achieved good performance with the recognition of traffic signs, even with the small ones. |
Description: | 67 Tr |
URI: | https://dspace.ctu.edu.vn/jspui/handle/123456789/73767 |
Appears in Collections: | Trường Công nghệ Thông tin & Truyền thông |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
_file_ Restricted Access | 41.71 MB | Adobe PDF | ||
Your IP: 18.118.37.224 |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.