  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 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 grading the images in RSOD into
four-difficulty levels. To our knowledge, RSOD is the first quantitatively
evaluated and graded snowy OD dataset. Then, we propose a novel Cross Fusion
(CF) block to construct a lightweight OD network based on YOLOv5s (call
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
https://github.com/qqding77/CF-YOLO-and-RSOD.
