AiOENet: All-in-One Low-Visibility Enhancement to Improve Visual Perception for Intelligent Marine Vehicles Under Severe Weather Conditions

Benefiting from the higher performance-cost ratio and installation convenience, the visible-light imaging camera has become one of the most widely-used onboard sensors for safe vehicle navigation. However, the captured images inevitably suffer from color distortion, contrast reduction, or loss of fine details under severe weather conditions (such as haze, low-lightness, rain, and snow). The quality-degraded visual information will lead to limited perceptual accuracy and range, resulting in the increased navigation risk for intelligent marine vehicles. To suppress the influences of severe imaging conditions on navigation safety, this work proposes an all-in-one low-visibility enhancement network (termed AiOENet) to improve the visual perception for marine surface vehicles under different weather scenarios. Specifically, our AiOENet mainly consists of a VGG16-driven scene discriminator, an encoder, parameter-shared Transformer blocks, and a decoder. The scene discriminator is exploited to classify four degradation types of low-visibility images. According to the classification results, the low-visibility images are fed into the corresponding encoders for coarse feature extraction. The multiple Transformer blocks are then employed to separate and extract the smaller-scale features. The latent normal-visibility images are finally generated through the corresponding decoders. Therefore, our AiOENet has the capacity of flexibly and adaptively restoring diverse low-visibility images using a uniform encoder-decoder network architecture. Compared with the state-of-the-art imaging methods, the AiOENet achieves comparable or even superior enhancement results in terms of both quantitative and qualitative evaluations. In addition, our method can contribute to more accurate and stable object detection with improved visual perception in maritime low-visibility scenes.