Traffic Light Recognition and Detection Based on Machine Learning
| dc.contributor.advisor | Sütő, József | |
| dc.contributor.author | Sun, Shilin | |
| dc.contributor.department | DE--Informatikai Kar | |
| dc.date.accessioned | 2025-02-22T20:33:59Z | |
| dc.date.available | 2025-02-22T20:33:59Z | |
| dc.date.created | 2024-11-03 | |
| dc.description.abstract | In this thesis, the traffic light was chosen as the main research object, the YOLOv5 framework For detection and identification. By improving contextual feature extraction and Highlight the small target detection function and improve the accuracy of traffic lights. And it can effectively guarantee the detection time.About detecting traffic lights based on machine learning. The current use and significance of traffic lights is examined. Create your own data set and use it with the official data set. Looking for a lighter model. | |
| dc.description.course | Programtervező informatikus | |
| dc.description.degree | BSc/BA | |
| dc.format.extent | 41 | |
| dc.identifier.uri | https://hdl.handle.net/2437/387338 | |
| dc.language.iso | en | |
| dc.rights.access | Hozzáférhető a 2022 decemberi felsőoktatási törvénymódosítás értelmében. | |
| dc.subject | Machine Learning | |
| dc.subject | YOLOv5 model | |
| dc.subject | Traffic Light Recognition and Detection | |
| dc.subject | Artificial Intelligence | |
| dc.subject | improved YOLOv5 | |
| dc.subject.dspace | Informatics::Computer Science | |
| dc.title | Traffic Light Recognition and Detection Based on Machine Learning |
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