LAUSR

According to statistics, more than 50% of optical satellite images are covered by clouds or fog. Moreover, the cloud cover rate is much higher in large bodies of water and nearby areas than inland areas due to the high amount of water evaporation and condensation. Therefore, ship detection from optical remote sensing images based on water surface analysis is more susceptible to cloud and fog interference, which affects the detection accuracy. In the time-sensitive application field of remote sensing images, due to the massive parameters in the large-scale network model, the detection speed is slow, and a lightweight detection model is commonly used. However, it is difficult for the lightweight detection model to achieve both high efficiency and accuracy for ship detection in a cloud-covered environment. To solve these problems, this manuscript proposes a lightweight algorithm called the fog remote sensing ship detection network (FRS-Net) suitable for ship detection from remote sensing images in thin-cloud and fog-covered environments. FRS-Net is developed based on the deep learning algorithm and can effectively improve ship detection accuracy under thin-cloud and fog-covered environment. First, for the allocation strategy of anchor boxes, by using the K-means clustering algorithm, FRS-Net simplifies the number of anchor boxes by utilizing the shape characteristics of the ship, which improves the time efficiency and the detection accuracy. Second, the FRS-Net network can meet the detection accuracy requirement and has a fast inference speed. The FRS-Net network is mainly composed of a backbone extraction network, a feature fusion network, and a prediction network. Experimental results on the ship detection in optical remote sensing image dataset demonstrate the detection accuracy and computational efficiency of FRS-Net. The recognition mean average precision achieved 43.20% for ship detection under thin-cloud and fog-covered environment, with an efficiency of up to 424 frames per second. FRS-Net has the potential to be applied in future scenarios such as embedded processing and onboard processing, where computing capabilities are strictly limited and the timeliness requirement is high.