CF-YOLO: Cross Fusion YOLO for Object Detection in Adverse Weather With a High-Quality Real Snow Dataset-Reference-Cited by-同舟云学术

CF-YOLO: Cross Fusion YOLO for Object Detection in Adverse Weather With a High-Quality Real Snow Dataset

Published:2023-10 Issue:10 Volume:24 Page:10749-10759
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Ding Qiqi¹^ORCID,Li Peng¹^ORCID,Yan Xuefeng¹^ORCID,Shi Ding¹,Liang Luming²^ORCID,Wang Weiming³^ORCID,Xie Haoran⁴^ORCID,Li Jonathan⁵^ORCID,Wei Mingqiang⁶^ORCID

Affiliation:

1. School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

2. Microsoft, Redmond, WA, USA

3. School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China

4. Department of Computing and Decision Sciences, Lingnan University, Hong Kong, China

5. Department of Geography and Environmental Management and the Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada

6. Shenzhen Research Institute, Nanjing University of Aeronautics and Astronautics, Shenzhen, China

Funder

National Natural Science Foundation of China

Shenzhen Science and Technology Program

Natural Science Foundation of Guangdong Province

Hong Kong Metropolitan University Research Grant

Direct Grant

Faculty Research Grant of Lingnan University, Hong Kong

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6979/10271405/10159575.pdf?arnumber=10159575

Reference43 articles.

1. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

2. DesnowNet: Context-Aware Deep Network for Snow Removal

3. Feature Pyramid Networks for Object Detection

4. Semantically Contrastive Learning for Low-Light Image Enhancement

5. R-FCN: Object detection via region-based fully convolutional networks;dai;Proc Adv Neural Inf Process Syst,2016

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