Maritime video surveillance of visual perception system has become an essential method to guarantee unmanned surface vessels (USV) traffic safety and security in maritime applications. However, when visual data are collected in a foggy marine environment, the essential optical information is often hidden in the fog, potentially resulting in decreased accuracy of ship detection. Therefore, a dual-channel and two-stage dehazing network (DTDNet) is proposed to improve the clarity and quality of the image to guarantee reliable ship detection under foggy conditions. Specifically, an upper and lower sampling structure is introduced to expand the original two-stage dehazing network into a two-channel network, to further capture the image features from different scale. Meanwhile, the attention mechanism is combined to provide different weights for different feature maps to maintain more image information. Furthermore, the perceptual function is constructed with the MSE-based loss function, so that it can better reduce the gap between the dehazing image and the unhazy image. Extensive experiments show that DTDNet has a better dehazing performance on both visual effects and quantitative index than other state-of-the-art dehazing networks. Moreover, the dehazing network is combined with the problem of ship detection under a sea-fog environment, and experiment results demonstrate that our network can be effectively applied to improve the visual perception performance of USV.

Image deraining ensures the visual quality of images to prompt ship detection for visual perception systems of unmanned surface vessels. However, due to the insufficiency of captured rain streaks features and global information, current image deraining methods often face the issues of rain streaks remaining and image blurring. Consider that the visual perception system captures the same useful information during rainy and hazy days, and only the way in which the image degrades is different. In addition, rainy days are usually accompanied by hazy days at the same time. In this paper, a two-stage and two-channel attention single image deraining network is proposed. Firstly, the subpixel convolution up-sampling module is introduced to increase the range of captured features and improve the image clarity. Secondly, the attention mechanism is integrated with the pyramid multi-scale pooling layer, so that the network can accumulate context information in a local to global way to avoid the loss of global information. In addition, a new composite loss function is designed, in which a regular term loss is introduced to maintain the smoothness and a perceptual loss function is employed to overcome the problem of large differences in the output of the loss function due to outliers. Extensive experimental results on both synthetic and real-world datasets demonstrate the superiority of our model in both quantitative assessments and visual quality by comparing with other state-of-the-art methods. Furthermore, the proposed deraining network is incorporated into the visual perception system and the detection accuracy of ships on rainy seas can be effectively improved.