Traffic Lights Detection and Recognition Method using Deep Learning with Improved YOLOv5 for Autonomous Vehicle in ROS2-Reference-Cited by-同舟云学术

Traffic Lights Detection and Recognition Method using Deep Learning with Improved YOLOv5 for Autonomous Vehicle in ROS2

Published:2023-02-24 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2023 8th International Conference on Intelligent Information Technology
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Author:

Hua Huy Khanh¹^ORCID,Nguyen Khang Hoang¹^ORCID,Quach Luyl-Da¹^ORCID,Tran Hoang Ngoc¹^ORCID

Affiliation:

1. Software Engineering, FPT University, Vietnam

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3591569.3591589

Reference21 articles.

1. W.-C. S. Cheng-Chin , Ming-Che Ho , “ Detecting and recognizing traffic lights by genetic approximate ellipse detection and spatial texture layouts ,” ICIC 2011 , 2011 . W.-C. S. Cheng-Chin, Ming-Che Ho, “Detecting and recognizing traffic lights by genetic approximate ellipse detection and spatial texture layouts,” ICIC 2011, 2011.

2. Real-time traffic light detection using color density

3. Traffic light mapping, localization, and state detection for autonomous vehicles

4. Combining Multiple Feature for Robust Traffic Sign Detection

5. You Only Look Once: Unified, Real-Time Object Detection

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