A Hybrid Approach for Efficient Traffic Sign Detection Using Yolov8 And SAM-Reference-Cited by-同舟云学术

A Hybrid Approach for Efficient Traffic Sign Detection Using Yolov8 And SAM

Published:2024-03-22 Issue: Volume:19 Page:298-302
ISSN:
Container-title:Proceedings of the 2024 3rd Asia Conference on Algorithms, Computing and Machine Learning
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Author:

Zhang Kai¹^ORCID,Chen Junzhou¹^ORCID,Zhang Ronghui¹^ORCID,Hu Chuan²^ORCID

Affiliation:

1. Sun Yat-Sen University, China

2. School of Mechanical Engineering, Shanghai Jiao Tong University, China

Funder

Guangzhou Science and Technology Plan Project

Nansha Key RD Program

Science and Technology Planning Project of Guangdong Province

Guangdong Basic and Applied Research Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3654823.3654878

Reference19 articles.

1. Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao. 2020. Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020).

2. A real-time and high-precision method for small traffic-signs recognition

3. Menglin Jia Luming Tang Bor-Chun Chen Claire Cardie Serge Belongie Bharath Hariharan and Ser-Nam Lim. 2022. Visual Prompt Tuning. http://arxiv.org/abs/2203.12119 arXiv:2203.12119 [cs].

4. Glenn Jocher. 2020. YOLOv5 by Ultralytics. https://doi.org/10.5281/zenodo.3908559

5. Glenn Jocher Ayush Chaurasia and Jing Qiu. 2023. Ultralytics YOLO. https://github.com/ultralytics/ultralytics

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