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

Qiqi DING, Peng LI, Xuefeng YAN, Luming LIANG, Weiming WANG, Haoran XIE, Jonathan LI, Mingqiang WEI

Research output: Journal PublicationsJournal Article (refereed)peer-review

5 Citations (Scopus)


Snow is one of the toughest adverse weather conditions for object detection (OD). Currently, not only there is a lack of snowy OD datasets to train cutting-edge detectors, but also these detectors have difficulties of learning latent information beneficial for detection in snow. To alleviate the two above problems, we first establish a real-world snowy OD dataset, named RSOD. Besides, we develop an unsupervised training strategy with a distinctive activation function, called Peak Act , to quantitatively evaluate the effect of snow on each object. Peak Act helps grade the images in RSOD into four-difficulty levels. To our knowledge, RSOD is the first quantitatively evaluated and graded real-world snowy OD dataset. Then, we propose a novel Cross Fusion (CF) block to construct a lightweight OD network based on YOLOv5s (called CF-YOLO). CF is a plug-and-play feature aggregation module, which integrates the advantages of Feature Pyramid Network and Path Aggregation Network in a simpler yet more flexible form. Both RSOD and CF lead our CF-YOLO to possess an optimization ability for OD in real-world snow. That is, CF-YOLO can handle unfavorable detection problems of vagueness, distortion and covering of snow. Experiments show that our CF-YOLO achieves better detection results on RSOD, compared to SOTAs. The code and dataset are available at
Original languageEnglish
Pages (from-to)10749-10759
Number of pages11
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number10
Early online date22 Jun 2023
Publication statusPublished - 1 Oct 2023

Bibliographical note

10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62172218)
Shenzhen Science and Technology Program (Grant Number: JCYJ20220818103401003 and JCYJ20220530172403007)
10.13039/501100003453-Natural Science Foundation of Guangdong Province (Grant Number: 2022A1515010170)
Hong Kong Metropolitan University Research (Grant Number: RD/2021/09)
Direct (Grant Number: DR23B2)
Faculty Research Grant of Lingnan University, Hong Kong (Grant Number: DB23A3 and DB23B2)

Publisher Copyright:

Publisher Copyright:
© 2000-2011 IEEE.


  • Detectors
  • Feature extraction
  • Kernel
  • Meteorology
  • Object detection
  • RSOD dataset
  • Snow
  • Training
  • cross fusion
  • peak act
  • snowy object detection


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