2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)最新文献_第10页

Line Segment Probability Alignment Direct Visual Odometer 线段概率对齐直接视觉里程表

2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE) Pub Date : 2022-12-16 DOI: 10.1109/ICARCE55724.2022.10046613

YuHang Wang, Cong Peng

引用次数: 0

Research on Worldview-3 Panchromatic and Shortwave Infrared Image Fusion Method Worldview-3全色与短波红外图像融合方法研究

2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE) Pub Date : 2022-12-16 DOI: 10.1109/ICARCE55724.2022.10046640

Qianqian Wang, Ying Bao

{"title":"Research on Worldview-3 Panchromatic and Shortwave Infrared Image Fusion Method","authors":"Qianqian Wang, Ying Bao","doi":"10.1109/ICARCE55724.2022.10046640","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046640","url":null,"abstract":"Worldview 3 is one of the most advanced high-resolution optical satellites. Aiming at the problems of large difference in spatial resolution between panchromatic band and short wave infrared (SWIR) band of Worldview 3 remote sensing satellite data and inconsistent spectral range, resulting in massive effect of fusion results and limited effect of spatial resolution enhancement, pannet network training is used for fusion. Firstly, the network reduces the spatial resolution of panchromatic band and realizes the preliminary integration with SWIR band; Then the preliminary fusion results are fused with the original resolution panchromatic band again. For spectral preservation, pannet adds the sampled multispectral image to the network output, which propagates the spectral information directly to the reconstructed image. The network trains the network parameters in the high pass filter domain rather than the image domain, so as to preserve the spatial structure. The results show that deep learning can achieve good results in image fusion. Pannet network structure can effectively enhance the spatial resolution of SWIR band, and also has a certain reference significance for the integration of traditional panchromatic and short wave infrared band.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132717419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Lightweight Conv-Swin Transformer for Wildlife Detection 用于野生动物检测的轻型逆变变压器

2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE) Pub Date : 2022-12-16 DOI: 10.1109/ICARCE55724.2022.10046623

Guobin Yang, Chenhong Sui, Fuhao Jiang, Yunhao Pan, Ankang Zang, Jian Hu

{"title":"Lightweight Conv-Swin Transformer for Wildlife Detection","authors":"Guobin Yang, Chenhong Sui, Fuhao Jiang, Yunhao Pan, Ankang Zang, Jian Hu","doi":"10.1109/ICARCE55724.2022.10046623","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046623","url":null,"abstract":"Wildlife detection is of great significance for wildlife monitoring and protection. Among existing object detection methods, Faster RCNN is a typical two-stage object detection method. In despite of its effectiveness, it suffers from the less satisfactory detection accuracy. This is mainly limited by the insufficient global representation of both objects and scenes. To this end, this paper proposes a lightweight Conv-Swin Transformer method for wildlife detection involving a lightweight combination of both convolution and Swin Transformer. In this study, Lightweight improvements are made in two main ways. The first one is done by reducing the number of Blocks in the third stage of the Swin Transformer; the second one is done by optimizing the down-sampling of different stages in the Swin Transformer network through the convolutional structure, which can speed up the detection of the model and improve the detection efficiency. The Faster RCNN model was chosen for experiments on a self-constructed wildlife dataset, using three different CNNs as well as the Swin Transformer as the backbone network for comparison. Experimental results show that the improved Conv-Swin Transformer, which combines the advantages of the attention mechanism and the convolutional structure, improves detection speed by 17.5% with a slight reduction in detection accuracy.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131065874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Graph Neural Network with Type-Feature Attention for Node Classification on Heterogeneous Graphs 基于类型特征关注的异构图节点分类图神经网络

2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE) Pub Date : 2022-12-16 DOI: 10.1109/ICARCE55724.2022.10046551

Kang Chen, Xueying Li, Tao Gong, Dehong Qiu

{"title":"A Graph Neural Network with Type-Feature Attention for Node Classification on Heterogeneous Graphs","authors":"Kang Chen, Xueying Li, Tao Gong, Dehong Qiu","doi":"10.1109/ICARCE55724.2022.10046551","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046551","url":null,"abstract":"Heterogeneous graphs are emerging as a prevalent form of data representation to capture complex structures and different relationships between a set of different types of objects in diverse disciplines. Node classification on heterogeneous graphs is a basic and critical task that remains unaddressed until the present day. Graph Neural Network is a powerful tool and has demonstrated remarkable performance in various tasks on graphs. However, most existing graph neural networks are based on the homophily assumption, which may be unsuitable for heterogeneous graphs. In this paper, we propose a graph neural network with type-feature attention mechanism to solve the problem of node classification on heterogeneous graphs. As a heterogeneous graph is composed of a group of edges between different types of nodes, it is reasonable to assume that each type of edge plays a different role in message propagation with different importance. An attention mechanism that considers the edge type and the features of the end nodes of the corresponding edge is built and incorporated into the process of message propagation of the graph neural network, by which the different heterogeneous information of nodes and edges is used jointly in solving the problem of node classification. We evaluate the proposed method on two public real-world heterogeneous graphs and the experimental results demonstrate the effectiveness of the proposed method.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131857683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dual-semantic Graph Similarity Learning for Image-text Matching 用于图像-文本匹配的双语义图相似学习

2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE) Pub Date : 2022-12-16 DOI: 10.1109/ICARCE55724.2022.10046452

Wenxin Tan, Hua Ji, Qian Liu, Ming Jin

{"title":"Dual-semantic Graph Similarity Learning for Image-text Matching","authors":"Wenxin Tan, Hua Ji, Qian Liu, Ming Jin","doi":"10.1109/ICARCE55724.2022.10046452","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046452","url":null,"abstract":"Image-text matching has received increasing attention because it enables the interaction between vision and language. Existing approaches have two limitations. First, most existing methods only pay attention to learning paired samples, ignoring the similar semantic information in the same modality. Second, the current methods lack interaction between local and global features, resulting in the mismatch of certain image regions or words due to the lack of global information. To solve the above problems, we propose a new dual semantic graph similarity learning (DSGSL) network, which consists of a feature enhancement module for learning compact features and a feature alignment module that learns the relations between global and local features. In the feature enhancement module, similar samples are processed as a graph, and a graph convolutional network is used to extract similar features to reconstruct the global feature representation. In addition, we use a gated fusion network to obtain discriminative sample representations by selecting salient features from other modalities and filtering out insignificant information. In the feature alignment module, we construct a dual semantic graph for every sample to learn the association between local features and global features. Numerous experiments on MS-COCO and Flicr30K have shown that our approach reaches the most advanced performance.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134318278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Oil Paper Insulation State Evaluation Method Integrating Fuzzy K-Nearest Neighbor and D-S Proof Theory 结合模糊k近邻和D-S证明理论的油纸绝缘状态评价方法

2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE) Pub Date : 2022-12-16 DOI: 10.1109/ICARCE55724.2022.10046550

Guangyong Chen, Yongqin Ke

引用次数: 0

A Novel Index-based Assessment Method for Rural Homestead Utilization 基于指标的农村宅基地利用评价新方法

2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE) Pub Date : 2022-12-16 DOI: 10.1109/ICARCE55724.2022.10046510

Chengquan Xu, Youwen Liu, Shaofei Jin

引用次数: 0

Northern Cthulhu Algorithm Optimized VMD Combined with SVM for Fault Diagnosis Northern Cthulhu算法优化的VMD与SVM相结合的故障诊断

2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE) Pub Date : 2022-12-16 DOI: 10.1109/ICARCE55724.2022.10046650

Dengxue Cao, Luyi Liu, Wei-ming Lin

引用次数: 0

Solar Filament Segmentation Based on AA-UNet 基于AA-UNet的太阳能灯丝分割

2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE) Pub Date : 2022-12-16 DOI: 10.1109/ICARCE55724.2022.10046547

Ya-Na Wu, Dan Liu, Xiangchun Liu

引用次数: 0

Design and Implementation of Manipulator Based on Arduino 基于Arduino的机械手设计与实现

2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE) Pub Date : 2022-12-16 DOI: 10.1109/ICARCE55724.2022.10046537

Xi Zhang, Yan Yang, Zhanyong Wei, Baoping Han

{"title":"Design and Implementation of Manipulator Based on Arduino","authors":"Xi Zhang, Yan Yang, Zhanyong Wei, Baoping Han","doi":"10.1109/ICARCE55724.2022.10046537","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046537","url":null,"abstract":"This paper first introduces the research background and significance of Chinese robotic arm research, and studies the current situation of Chinese domestic robotic arm research and development, and integrates Arduino technology and robotic arm design. Therefore, this paper gives the design of Chinese robotic arm based on Arduino technology. This paper will first introduce the development tools, overall structure and principle required for the design, and use ArduinoIDE software to program, and analyze the forward kinematics and inverse kinematics of the four-degree-of-freedom motion. The detailed design of the mechanical arm is made. The hardware selects Arduino UNO R3, the mechanical arm selects a four-degree-of-freedom mechanical arm that can be combined, the main control panel selects the Arduino UNO board, and the control module selects the PS2 rocker. It can adapt to the industrial needs of miniaturization, low power consumption and rapid development, and provides a useful reference for small enterprises to promote the application of low-cost micro four-degree-of-freedom manipulators on production lines.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114723284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0