Improved denoising autoencoder for maritime image denoising and semantic segmentation of USV

Unmanned surface vehicle (USV) is currently a hot research topic in maritime communication network (MCN), where de-noising and semantic segmentation of maritime images taken by USV have been rarely studied. The former has recently researched on autoencoder model used for image denoising, but the existed models are too complicated to be suitable for real-time detection of USV. In this paper, we proposed a lightweight autoencoder combined with inception module for maritime image denoising in different noisy environments and explore the effect of different inception modules on the denoising performance. Furthermore, we completed the semantic segmentation task for maritime images taken by USV utilizing the pretrained U-Net model with tuning, and compared them with original U-Net model based on different backbone. Subsequently, we compared the semantic segmentation of noised and denoised maritime images respectively to explore the effect of image noise on semantic segmentation performance. Case studies are provided to prove the feasibility of our proposed denoising and segmentation method. Finally, a simple integrated communication system combining image denoising and segmentation for USV is shown.