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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 traincutting-edge detectors, but also these detectors have difficulties learninglatent information beneficial for detection in snow. To alleviate the two aboveproblems, we first establish a real-world snowy OD dataset, named RSOD.Besides, we develop an unsupervised training strategy with a distinctiveactivation function, called $Peak \ Act$, to quantitatively evaluate the effectof snow on each object. Peak Act helps grading the images in RSOD intofour-difficulty levels. To our knowledge, RSOD is the first quantitativelyevaluated and graded snowy OD dataset. Then, we propose a novel Cross Fusion(CF) block to construct a lightweight OD network based on YOLOv5s (callCF-YOLO). CF is a plug-and-play feature aggregation module, which integratesthe advantages of Feature Pyramid Network and Path Aggregation Network in asimpler yet more flexible form. Both RSOD and CF lead our CF-YOLO to possess anoptimization ability for OD in real-world snow. That is, CF-YOLO can handleunfavorable 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 athttps://github.com/qqding77/CF-YOLO-and-RSOD.

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