Traffic Light Detection for Self-Driving Cars using the YOLOv8 architecture
Yazarlar (7)
Aditya Gupta Sharda University, Hindistan
Ayushi Gupta Sharda University, Hindistan
Gaurav Raj Sharda University, Hindistan
Tanupriya Choudhury Graphic Era Deemed To Be University, Hindistan
Ayan Sar University Of Petroleum And Energy Studies, Hindistan
Ketan Kotecha Symbiosis Institute Of Technology, Hindistan
Prof. Dr. Turgut ÖZSEVEN Tokat Gaziosmanpaşa Üniversitesi, Türkiye
| Bildiri Türü |
Tebliğ/Bildiri
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Bildiri Dili | İngilizce |
| Bildiri Alt Türü | Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum) | ||
| Bildiri Niteliği | Alanında Hakemli Uluslararası Kongre/Sempozyum | ||
| DOI Numarası | 10.1145/3660853.3660925 | ||
| Kongre Adı | Cognitive Models and Artificial Intelligence Conference | ||
| Kongre Tarihi | 25-05-2024 / 26-05-2024 | ||
| Basıldığı Ülke | Türkiye | Basıldığı Şehir | İstanbul |
| Bildiri Linki | http://dx.doi.org/10.1145/3660853.3660925 | ||
| UAK Araştırma Alanları |
Mühendislik
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| Özet |
| A traffic light detection dataset is used to evaluate the performance of the latest version of YOLOv8. Using this dataset and an ideal you only look at once (yolo), we can create a data-driven traffic light detection system that has both high detection accuracy and high performance during the training and detection process. The number of accidents caused by failure to obey traffic rules and traffic lights has increased dramatically. Self-driving cars are the solution to 90% of all traffic accidents, but the software must be efficient, fast, and reliable to accurately detect traffic signs. YOLO is the most advanced object recognition technology. |
| Anahtar Kelimeler |
| Computer Vision | Traffic Lights | YOLOv8 |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 1 |
| Google Scholar | 2 |