Unmanned surface vehicle(USV)is currently a hot research topic in maritime communication network(MCN),where denoising and semantic segmentation of maritime images taken by USV have been rarely studied.The former has r...Unmanned surface vehicle(USV)is currently a hot research topic in maritime communication network(MCN),where denoising 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.展开更多
In recent years,various maritime applications such as unmanned surface vehicles,marine environment monitoring,target tracking,and emergency response have developed rapidly in maritime communication networks(MCNs),and ...In recent years,various maritime applications such as unmanned surface vehicles,marine environment monitoring,target tracking,and emergency response have developed rapidly in maritime communication networks(MCNs),and these applications are often accompanied by complex computation tasks and low latency requirements.However,due to the limited resources of the vessels,it is critical to design an efficient mobile edge computing(MEC)enabled network for maritime computation.Inspired by this motivation,energy harvesting space-air-sea integrated networks(EH-SASINs)for maritime computation tasks offloading are proposed in this paper.We first make the optimal deployment of tethered aerostats(TAs)with the K-means method.In addition,we study the issue of computation task offloading for vessels,focusing on minimizing the process delay of computation task based on the proposed architecture.Finally,because of the NP-hard properties of the optimization problem,we solve it in two stages and propose an improved water-filling algorithm based on queuing theory.Simulation results show that the proposed EHSASINs and algorithms outperform the existing scenarios and can reduce about 50%of the latency compared with local computation.展开更多
基金Natural Science Foundation of Fujian Province(No.2019J05026)in part by the Education Scientific Research Project for Young Teachers of Fujian Province(No.JT180053).
文摘Unmanned surface vehicle(USV)is currently a hot research topic in maritime communication network(MCN),where denoising 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.
基金supported in part by 2020 Science and Technology Innovation Team from Universities of Fujian Province,the NSF of China(Nos.61871132,62171135)the Project of Science and Technology of Quanzhou City 2021N050。
文摘In recent years,various maritime applications such as unmanned surface vehicles,marine environment monitoring,target tracking,and emergency response have developed rapidly in maritime communication networks(MCNs),and these applications are often accompanied by complex computation tasks and low latency requirements.However,due to the limited resources of the vessels,it is critical to design an efficient mobile edge computing(MEC)enabled network for maritime computation.Inspired by this motivation,energy harvesting space-air-sea integrated networks(EH-SASINs)for maritime computation tasks offloading are proposed in this paper.We first make the optimal deployment of tethered aerostats(TAs)with the K-means method.In addition,we study the issue of computation task offloading for vessels,focusing on minimizing the process delay of computation task based on the proposed architecture.Finally,because of the NP-hard properties of the optimization problem,we solve it in two stages and propose an improved water-filling algorithm based on queuing theory.Simulation results show that the proposed EHSASINs and algorithms outperform the existing scenarios and can reduce about 50%of the latency compared with local computation.
