CCSPNet-Joint: Efficient Joint Training Method for Traffic Sign Detection Under Extreme Conditions-Reference-Cited by-同舟云学术

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CCSPNet-Joint: Efficient Joint Training Method for Traffic Sign Detection Under Extreme Conditions

Published:2024-06-30 Issue: Volume:12 Page:1-8
ISSN:
Container-title:2024 International Joint Conference on Neural Networks (IJCNN)
language:
Short-container-title:

Author:

Hong Haoqin¹,Zhou Yue¹,Shu Xiangyu²,Hu Xiaofang³

Affiliation:

1. Southwest University,Institute of Innovation & Entrepreneurship, Hanhong College,Chongqing,China

2. Southwest University,College of Computer and Information Science,Chongqing,China

3. Southwest University,School of Artificial Intelligence,Chongqing,China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Chongqing

Fundamental Research Funds for the Central Universities

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10649807/10649898/10651346.pdf?arnumber=10651346

Reference36 articles.

1. Yolov4: Optimal speed and accuracy of object detection;Bochkovskiy,2020

2. End-to-End Object Detection with Transformers

3. Hybrid Task Cascade for Instance Segmentation

4. An Accurate Small Signal Dynamic Model for LCC-HVDC

5. An image is worth 16x16 words: Transformers for image recognition at scale;Dosovitskiy

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