Image data acquired by unmanned surface vehicle (USV) perception systems in hazy situations is characterized by low resolution and low contrast, which can seriously affect subsequent high-level vision tasks. To obtain high-definition images under maritime hazy conditions, an end-to-end multi-branch gated fusion network (MGFNet) is proposed. Firstly, residual channel attention, residual pixel attention, and residual spatial attention modules are applied in different branch networks. These attention modules are used to focus on high-frequency image details, thick haze area information, and contrast enhancement, respectively. In addition, the gated fusion subnetworks are proposed to output the importance weight map corresponding to each branch, and the feature maps of three different branches are linearly fused with the importance weight map to help obtain the haze-free image. Then, the network structure is evaluated based on the comparison with pertinent state-of-the-art methods using artificial and actual datasets. The experimental results demonstrate that the proposed network is superior to other previous state-of-the-art methods in the PSNR and SSIM and has a better visual effect in qualitative image comparison. Finally, the network is further applied to the hazy sea–skyline detection task, and advanced results are still achieved.

In this paper, we release a new large-scale dataset containing multiple categories of ships and floating objects at sea, which we call MASS-LSVD. It is used to train and validate target detection algorithms and future large models for ship autopiloting. The dataset was captured by a visible light camera installed aboard the world’s first intelligent research, teaching, and training ship, “Xinhongzhuan”. This MASS (maritime autonomous surface ship) was operated by Dalian Maritime University, China. We have collected more than 4000 h of video of the “Xinhongzhuan” vessel’s voyage in the Bohai Sea and other areas, which are carefully classified and filtered to cover as much as possible the various types of sample data in the marine environment, such as light intensity, weather, hull shading, data from ocean-going voyages, entering and exiting ports, etc. The dataset contains 64,263 1K-resolution images captured from video footage, covering four main ship types: Fishing Boat, Bulk Carrier, Cruise Ship, Container ship, and an ‘Other Ships’ class, for vessels that cannot be specifically classified. The dataset currently contains 64,263 pairs of 1K-resolution images covering four common ship types (fishing boat, bulk carrier, cruise ship, container, and other ship, where no specific ship type can be determined). All the images have been labeled with high-precision manual bounding boxes. In this paper, the MASS-LSVD dataset is used as the basis for training various target detection algorithms and comparing them with other datasets, which compensates for the lack of first-view images in the vessel target detection dataset, and MASS-LSVD is expected to be used to facilitate the research and application of autonomous ship navigation models in the framework of computer vision.

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.