A method for detecting navigable areas in narrow rivers under complex reflection conditions

Abstract

The perception of unmanned surface vehicles is significantly influenced by the detection of navigable areas in narrow rivers. Conventional semantic segmentation networks are unable to resolve the numerous interferences on the water's surface, including highlights and inverted images. To solve this problem, a river surface image reflection removal generative adversarial network (RRGAN) is proposed to eliminate the interference of harsh water surface environment. The proposed RRGAN only uses a single generator to reduce the number of parameters. By adding AdaLIN layers in the generator to enhance the ability to generate low‐reflection images, the AdaLIN encoder (AdaLINE) is proposed to automatically generate normalized affine parameters. In addition, a cycle semantic consistency loss function with a single generator is proposed to ensure that the water region of the generated images remains unchanged. Finally, a two‐stage method for detecting navigable areas is proposed. In the first stage, the RRGAN is used to remove the interference on the water surface environment. In the second stage, the semantic segmentation network is used to segment the water body from the denoised image to determine the navigable areas on the water surface. The experimental results demonstrate that, in the complex and varied narrow river environment, the suggested RRGAN method can significantly reduce the reflection interference of the water surface and improve the accuracy of the water segmentation after the reflection is removed.