A Lightweight Traffic Sign Detection Method With Improved YOLOv7-Tiny-Reference-Cited by-同舟云学术

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A Lightweight Traffic Sign Detection Method With Improved YOLOv7-Tiny

Published:2024 Issue: Volume:12 Page:105131-105147
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Cao Xiaobing¹^ORCID,Xu Yicen²,He Jiawei¹,Liu Jiahui³,Wang Yongjie³^ORCID

Affiliation:

1. School of Control Engineering, Wuxi Institute of Technology, Wuxi, China

2. School of Intelligent Equipment and Automotive Engineering, Wuxi Vocational Institute of Commerce, Wuxi, China

3. College of Computer and Control Engineering, Northeast Forestry University, Harbin, China

Funder

Qinglan Project of Jiangsu Province of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10614180.pdf?arnumber=10614180

Reference44 articles.

1. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

2. Real-Time Traffic Sign Recognition Based on Efficient CNNs in the Wild

3. Deep Learning for Large-Scale Traffic-Sign Detection and Recognition

4. A Hierarchical Deep Architecture and Mini-batch Selection Method for Joint Traffic Sign and Light Detection

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