微地图作为自媒体时代出现的新型地图,具有大众参与、个性化、快速传播等特征,然而现有微地图研究在点符号设计方面存在一定局限,难以完全满足大众个性化制图的需求。为解决这一问题,本文从微地图制作角度出发,选择手绘地图的通用地图...微地图作为自媒体时代出现的新型地图,具有大众参与、个性化、快速传播等特征,然而现有微地图研究在点符号设计方面存在一定局限,难以完全满足大众个性化制图的需求。为解决这一问题,本文从微地图制作角度出发,选择手绘地图的通用地图符号作为研究对象,构建了一个包含多种类型和样式的手绘地图数据集。在现有研究的基础上,通过对比选择目标检测中常用的YOLOv5(You Only Look Once v5)系列模型,深入探索手绘地图中通用地图符号的自动提取方法,并采用YOLOv5-X模型进行手绘地图通用地图符号的提取。实验结果显示,该模型在手绘地图数据集上的point类别提取精确度、召回率和F1得分分别达到了98.42%、94.72%和97%。同时在Quick Draw涂鸦数据集上进行模型泛化能力的测试,本文所使用的模型在该数据集上展现出良好的提取效果。本研究的开展不仅扩充了微地图个性化点符号的研究数据集,还改进了通用地图符号的提取方法,为微地图制图注入了更多元化的元素,也为自媒体时代的地图制作提供了更为灵活和个性化的解决方案。展开更多
With the advancement of technology and the increase in user demands, gesture recognition played a pivotal role in the field of human-computer interaction. Among various sensing devices, Time-of-Flight (ToF) sensors we...With the advancement of technology and the increase in user demands, gesture recognition played a pivotal role in the field of human-computer interaction. Among various sensing devices, Time-of-Flight (ToF) sensors were widely applied due to their low cost. This paper explored the implementation of a human hand posture recognition system using ToF sensors and residual neural networks. Firstly, this paper reviewed the typical applications of human hand recognition. Secondly, this paper designed a hand gesture recognition system using a ToF sensor VL53L5. Subsequently, data preprocessing was conducted, followed by training the constructed residual neural network. Then, the recognition results were analyzed, indicating that gesture recognition based on the residual neural network achieved an accuracy of 98.5% in a 5-class classification scenario. Finally, the paper discussed existing issues and future research directions.展开更多
文摘微地图作为自媒体时代出现的新型地图,具有大众参与、个性化、快速传播等特征,然而现有微地图研究在点符号设计方面存在一定局限,难以完全满足大众个性化制图的需求。为解决这一问题,本文从微地图制作角度出发,选择手绘地图的通用地图符号作为研究对象,构建了一个包含多种类型和样式的手绘地图数据集。在现有研究的基础上,通过对比选择目标检测中常用的YOLOv5(You Only Look Once v5)系列模型,深入探索手绘地图中通用地图符号的自动提取方法,并采用YOLOv5-X模型进行手绘地图通用地图符号的提取。实验结果显示,该模型在手绘地图数据集上的point类别提取精确度、召回率和F1得分分别达到了98.42%、94.72%和97%。同时在Quick Draw涂鸦数据集上进行模型泛化能力的测试,本文所使用的模型在该数据集上展现出良好的提取效果。本研究的开展不仅扩充了微地图个性化点符号的研究数据集,还改进了通用地图符号的提取方法,为微地图制图注入了更多元化的元素,也为自媒体时代的地图制作提供了更为灵活和个性化的解决方案。
文摘With the advancement of technology and the increase in user demands, gesture recognition played a pivotal role in the field of human-computer interaction. Among various sensing devices, Time-of-Flight (ToF) sensors were widely applied due to their low cost. This paper explored the implementation of a human hand posture recognition system using ToF sensors and residual neural networks. Firstly, this paper reviewed the typical applications of human hand recognition. Secondly, this paper designed a hand gesture recognition system using a ToF sensor VL53L5. Subsequently, data preprocessing was conducted, followed by training the constructed residual neural network. Then, the recognition results were analyzed, indicating that gesture recognition based on the residual neural network achieved an accuracy of 98.5% in a 5-class classification scenario. Finally, the paper discussed existing issues and future research directions.
