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Sea fog significantly threatens the safety of maritime activities. This article develops a sea fog detection dataset (SFDD) and a dual-branch sea fog detection network (DB-SFNet). We investigate all the observed sea fog events in the Yellow Sea and the Bohai Sea (118.1&#x00B0;E&#x2013;128.1&#x00B0;E, 29.5&#x00B0;N&#x2013;43.8&#x00B0;N) from 2010 to 2020 and collect the sea fog images for each event from the Geostationary Ocean Color Imager (GOCI) to comprise the dataset SFDD. The location of the sea fog in each image in SFDD is accurately marked. The proposed dataset is characterized by a long-time span, a large number of samples, and accurate labeling, which can substantially improve the robustness of various sea fog detection models. Furthermore, this article proposes a DB-SFNet to achieve accurate and holistic sea fog detection. The proposed DB-SFNet is composed of a knowledge extraction module and a dual-branch optional encoding decoding module. The two modules jointly extract discriminative features from both visual and statistical domains. Experiments show promising sea fog detection results with an F1-score of 0.77 and a critical success index of 0.63. Compared with existing advanced deep learning networks, DB-SFNet is superior in detection performance and stability, particularly in the mixed cloud and fog areas.

Dual-Branch Neural Network for Sea Fog Detection in Geostationary Ocean Color Imager