FedGCN: Federated Learning-Based Graph Convolutional Networks for Non-Euclidean Spatial Data

Hu, Kai; Wu, Jiasheng; Li, Yaogen; Lu, Meixia; Weng, Liguo; Xia, Min

doi:10.3390/math10061000

Cited by 28 publications

(16 citation statements)

References 20 publications

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“…Modern semantic VSLAM systems cannot do without the help of deep learning, and feature attributes and association relations obtained through learning can be used in different tasks [138]. As an important branch of machine learning, deep learning has achieved remarkable results in image recognition [139], semantic understanding [140], image matching [141], 3D reconstruction [142], and other tasks. The application of deep learning in computer vision can greatly ease the problems encountered by traditional methods [143].…”

Section: Semantic Vslammentioning

confidence: 99%

An Overview on Visual SLAM: From Tradition to Semantic

et al. 2022

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Visual SLAM (VSLAM) has been developing rapidly due to its advantages of low-cost sensors, the easy fusion of other sensors, and richer environmental information. Traditional visionbased SLAM research has made many achievements, but it may fail to achieve wished results in challenging environments. Deep learning has promoted the development of computer vision, and the combination of deep learning and SLAM has attracted more and more attention. Semantic information, as high-level environmental information, can enable robots to better understand the surrounding environment. This paper introduces the development of VSLAM technology from two aspects: traditional VSLAM and semantic VSLAM combined with deep learning. For traditional VSLAM, we summarize the advantages and disadvantages of indirect and direct methods in detail and give some classical VSLAM open-source algorithms. In addition, we focus on the development of semantic VSLAM based on deep learning. Starting with typical neural networks CNN and RNN, we summarize the improvement of neural networks for the VSLAM system in detail. Later, we focus on the help of target detection and semantic segmentation for VSLAM semantic information introduction. We believe that the development of the future intelligent era cannot be without the help of semantic technology. Introducing deep learning into the VSLAM system to provide semantic information can help robots better perceive the surrounding environment and provide people with higher-level help.

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Section: Semantic Vslammentioning

confidence: 99%

An Overview on Visual SLAM: From Tradition to Semantic

et al. 2022

Self Cite

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“…The graph convolution neural network (GCN) [ 18 , 19 , 20 , 21 ] summarized the convolution operation from grid image data to graph data with a topological structure. Its main idea was to aggregate the characteristics of its nodes and the characteristics of neighbor nodes, coupled with the natural constraints of the topological graph so that new node characteristics could be generated.…”

Section: Related Workmentioning

confidence: 99%

Multiple Attention Mechanism Graph Convolution HAR Model Based on Coordination Theory

Ding

Jin

et al. 2022

Sensors

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Human action recognition (HAR) is the foundation of human behavior comprehension. It is of great significance and can be used in many real-world applications. From the point of view of human kinematics, the coordination of limbs is an important intrinsic factor of motion and contains a great deal of information. In addition, for different movements, the HAR algorithm provides important, multifaceted attention to each joint. Based on the above analysis, this paper proposes a HAR algorithm, which adopts two attention modules that work together to extract the coordination characteristics in the process of motion, and strengthens the attention of the model to the more important joints in the process of moving. Experimental data shows these two modules can improve the recognition accuracy of the model on the public HAR dataset (NTU-RGB + D, Kinetics-Skeleton).

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“…These systems can be effectively used in various fire detection scenarios. Notably, deep learning-based methods have demonstrated robust learning capabilities and scalability [11], rendering them widely applicable in the field of fire detection. With the continuous advancements in deep learning and convolutional neural networks [12], vision-based object detection techniques have achieved remarkable accuracy.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Forest Fire Detection Algorithm Using Improved YOLOv5

Shi,

Lu,

Wang

et al. 2023

Forests

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Forest fires result in severe disaster, causing significant ecological damage and substantial economic losses. Flames and smoke represent the predominant characteristics of forest fires. However, these flames and smoke often exhibit irregular shapes, rendering them susceptible to erroneous positive or negative identifications, consequently compromising the overall performance of detection systems. To enhance the average precision and recall rates of detection, this paper introduces an enhanced iteration of the You Only Look Once version 5 (YOLOv5) algorithm. This advanced algorithm aims to achieve more effective fire detection. First, we use Switchable Atrous Convolution (SAC) in the backbone network of the traditional YOLOv5 to enhance the capture of a larger receptive field. Then, we introduce Polarized Self-Attention (PSA) to improve the modeling of long-range dependencies. Finally, we incorporate Soft Non-Maximum Suppression (Soft-NMS) to address issues related to missed detections and repeated detections of flames and smoke by the algorithm. Among the plethora of models explored, our proposed algorithm achieves a 2.0% improvement in mean Average Precision@0.5 (mAP50) and a 3.1% enhancement in Recall when compared with the YOLOv5 algorithm. The integration of SAC, PSA, and Soft-NMS significantly enhances the precision and efficiency of the detection algorithm. Moreover, the comprehensive algorithm proposed here can identify and detect key changes in various monitoring scenarios.

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FedGCN: Federated Learning-Based Graph Convolutional Networks for Non-Euclidean Spatial Data

Cited by 28 publications

References 20 publications

An Overview on Visual SLAM: From Tradition to Semantic

An Overview on Visual SLAM: From Tradition to Semantic

Multiple Attention Mechanism Graph Convolution HAR Model Based on Coordination Theory

An Efficient Forest Fire Detection Algorithm Using Improved YOLOv5

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