Ship instance segmentation is essential for intelligent maritime navigation and traffic safety. However, under adverse weather conditions such as fog, the image quality from imaging devices degrades significantly, lea...
Ship instance segmentation is essential for intelligent maritime navigation and traffic safety. However, under adverse weather conditions such as fog, the image quality from imaging devices degrades significantly, leading to poor performance of existing instance segmentation methods. To address this challenge, we propose FA YOLO, a ship instance segmentation framework based on interference suppression and feature refinement designed to enhance performance under foggy conditions. First, we propose a Multi-Scale Feature Aggregation Mamba (MFAM) module, which utilizes a state space modeling approach and a multi-scale channel aggregation gating mechanism to enhance long-range dependency modeling and global contextual representation. Second, we propose an Adaptive Fog Dehazing Module (AFDM), which utilizes parallel channel and spatial attention mechanisms along with a window-based multi-head self-attention strategy to suppress fog-related interference and improve focus on target regions. Third, we propose a Multi-Scale Perception-Guided Attention Module (MPAM), which integrates channel-position attention fusion, multi-window branch feature extraction and similarity measurement strategies to adaptively enhance and aggregate multi-scale contextual features. In addition, to address the lack of suitable foggy ship instance segmentation datasets in the community, we collected and annotated a new instance segmentation dataset of maritime ships under foggy conditions, FSISD. This dataset contains 10,249 ship images, covering common ship categories and environmental conditions. Experimental results on Foggy Cityscapes, FSISD and Foggy COCO-boat demonstrate that FA YOLO outperforms the baseline YOLOv8s in segmentation accuracy by 3.3%, 2.2% and 1.3%, respectively, confirming superior performance and strong generalization capability.
Energy systems are currently undergoing a transformation towards new paradigms characterized by decarbonization,decentralization,democratization,and *** this evolving context,energy blockchain,aiming to enhance effici...
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Energy systems are currently undergoing a transformation towards new paradigms characterized by decarbonization,decentralization,democratization,and *** this evolving context,energy blockchain,aiming to enhance efficiency,transparency,and security,emerges as an integrated technological solution designed to address the diverse challenges in this *** security is essential for the reliable and efficient functioning of energy *** pressing need to address challenges related to secure data storage,effective data management,and efficient data utilization is increasingly *** paper offers a comprehensive survey of academic discourse on energy blockchain data security over the past five years,adopting an all-encompassing perspective that spans data storage,management,and *** work systematically evaluates and contrasts the strengths and weaknesses of various research ***,this paper proposes an integrated hierarchical on-chain and off-chain security energy blockchain architecture,specifically designed to meet the complex security requirements of multi-blockchain business ***,this paper identifies key directions for future research,particularly in advancing the integration of storage,management,and utilization of energy blockchain data security.
Pedestrian trajectory prediction plays a pivotal role in ensuring the safety and efficiency of various applications, including autonomous vehicles and traffic management systems. This paper proposes a novel method for...
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Human pose estimation is a challenging task that requires the comprehension of the pose structure. This work can refer to spatial relation inference in a pose structure model;how to model the dynamic spatial relation ...
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In edge computing (EC), resource allocation is to allocate computing, storage and networking resources on the edge nodes (ENs) efficiently and reasonably to tasks generated by users. Due to the resource-limitation of ...
In edge computing (EC), resource allocation is to allocate computing, storage and networking resources on the edge nodes (ENs) efficiently and reasonably to tasks generated by users. Due to the resource-limitation of ENs, the tasks often need to compete for the resources. Pricing mechanisms are widely used to deal with the resource allocation problem, and the valuations of tasks play a critical role in the price mechanisms. However, users naturally are not willing to expose the valuations of their tasks due to conflicts of interests. Current research works usually adopt truthful auctions to motivate the users to report honestly the valuations of their tasks. In this paper, we introduce the marginal value to estimate the valuations of tasks, and propose a marginal value-based pricing mechanism using the incentive theory, which motivates the tasks with higher marginal values to actively request more resources. The EC platform sets the resource prices using the price mechanism, and then the users determine their resource requests relying on the resource prices and the valuations of their tasks. After receiving the deadline-sensitive tasks from the users, the resource allocation can be modeled as a knapsack problem with the deadline constraints. Extensive experimental results demonstrate that our approach is computationally efficient and is promising in enhancing the utility of the EC platform and the tasks.
A SnF scheduling method is presented to schedule data transfers in the HFL aggregation process across the ECPON. Studies demonstrate that the proposed method outperforms conventional methods in terms of network perfor...
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Electronic Medical Records (EMR) and other medical data contain important and sensitive privacy information of patients, which provide important basis and reference for their doctors to diagnose and treat them. With t...
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Previous studies have discovered that Transformers pre-trained on large scale datasets have a great advantage over traditional methods and can achieve human-level performance in many NLP tasks. Various encoder-only mo...
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In this paper, an STC-SnF approach is presented to schedule bulk data transfers across the CPON. Studies show it can ensure the coordinated space-time relation of bandwidth fragments and hence benefit the SnF scheduli...
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