Traffic Light Detection for Self-Driving Cars using the YOLOv8 architecture-Reference-Cited by-同舟云学术

Traffic Light Detection for Self-Driving Cars using the YOLOv8 architecture

Published:2024-05-25 Issue: Volume:1 Page:263-269
ISSN:
Container-title:Proceedings of the Cognitive Models and Artificial Intelligence Conference
language:
Short-container-title:

Author:

Gupta Aditya¹^ORCID,Gupta Ayushi¹^ORCID,Raj Gaurav¹^ORCID,Choudhury Tanupriya²^ORCID,Sar Ayan³^ORCID,Kotecha Ketan⁴^ORCID,Ozseven Turgut⁵^ORCID

Affiliation:

1. Sharda University, India

2. CSE Dept., Graphic Era Deemed to be University, India

3. University of Petroleum and Energy Studies, India

4. Symbiosis Institute of Technology, Lavale, Pune, 412115, India., Symbiosis Centre for Applied Artificial Intelligence (SCAAI), Symbiosis International (Deemed University) (SIU), India

5. Tokat Gaziosmanpasa University, Turkiye

Funder

We thank to receive the funding for this research work from RSF (Research Support funding) from Symbiosis Institute of Technology, Symbiosis International (Deemed University) (SIU),Lavale Campus, Pune, Maharashtra,412115, India.

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3660853.3660925

Reference7 articles.

1. Traffic sign recognition based on SVM and convolutional neural network

2. Traffic Sign Detection and Recognition using a CNN Ensemble

3. Sudha S. K. Mahathi L 2016 A Real-Time System for Detection and Recognition of Traffic Signs INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) NCETET – 2016 (Volume 4 – Issue 17)

4. Hasan, Nazmul & Anzum, Tanvir & Jahan, Nusrat. (2020). Traffic Sign Recognition System (TSRS): SVM and Convolutional Neural Network. 10.1007/978-981-15-7345-3_6.

5. Automatic traffic sign detection and recognition in video sequences