International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Framework for Training Speed Sign Detection Vehicle Using Yolo Algorithm for Detection Purposes of Autonomous Self Driving Cars

Abstract

In 21’st century we have witnessed a vast development in the field of Autonomous Self driving cars and automation in various auto industries. It is not far when we can see fully self-driving cars traveling from destination A to destination B without any human interference, and we can have a gist of that when Elon Musk introduced the first semi-autonomous TESLA vehicle. In this ever-fast-growing autonomous industry, we have tried to contribute to this industry by integrating OpenCV with python to test our first autonomous model in the CARLA environment which will detect speed signs and control the speed of the vehicle accordingly. We will use the YOLO object detection algorithm for the purpose of Speed sign detection and train our model on the same, we have gathered various images of Speed signs from the CARLA environment which will be used as a strong dataset for the training purpose. 

Country : India

1 Sheetal Kulkarni

  1. Associate Professor, Department of Computer Science and Engineering, Malla Reddy College of Engineering for Women, Hyderabad -500100, Telangana, India

IRJIET, Volume 2, Issue 3, May 2018 pp. 46-50

.

Full Paper
Download

References

  1. Chengji Liu ,Yufan Tao ,Jiawei Liang ,Kai Li1 ,Yihang Chen," Object Detection Based on YOLO Network" 2018 IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC 2018)
  2. Wenbo Lan, Jianwu Dang, Yangping Wang and Song Wang," Pedestrian Detection Based on YOLO Network Model" 978-1-5386-60751/18/$31.00 ©2018 IEEE
  3. Zhimin Mo1, Liding Chen1, Wenjing You1" Identification and Detection of Automotive Door Panel Solder Joints based on YOLO" 978-1-72810106-4/19$31.00 ©2019 IEEE
  4. Rumin Zhang, Yifeng Yang," An Algorithm for Obstacle Detection based on YOLO and Light Filed Camera", 2018 Twelfth International Conference on Sensing Technology (ICST)
  5. J.R.R. Uijling, K.E.A. van de Sande, T. Gever, and A.W.M. Smeulders2 "Selective Search for Object Recognition" in International Journal of Computer Vision • September 2013
  6. Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" arXiv:1506.01497v3 [cs.CV] 6 Jan 2016
  7. Joseph Redmon,Santosh Divvala,Ross Girshick,Ali Farhadi." You Only Look Once: Unified, Real-Time Object Detection" [J].2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2016:779788.
  8. Kumar S. Ray and Soma Chakraborty "An Efficient Approach for Object Detection and Tracking of Objects in a Video with Variable Background" 2017 arXiv:1706.02672
  9. Eric Ke Wang, Yueping Li, Zhe Nie, Juntao Yu, Zuodong Liang, Xun Zhang, Siu Ming Yiu "Deep Fusion Feature Based Object Detection Method for High Resolution Optical Remote Sensing Images"
  10. Womg A, Shafiee MJ, Li F, Chwyl B. Tiny SSD: a tiny singleshot detection deep convolutional neural network for real-time embedded object detection. In: 2018 15th conference on computer and robot vision (CRV). IEEE; 2018, p. 95101
  11. Xinyi Zhou, Wei Gong, WenLong Fu, Fengtong Du "Application of Deep Learning in Object Detection" 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS) 

Copyright @ 2026-2017 IRJIET. All Right Reserved.

Developed by Byzero Technologies