为提升巡检机器人的导航避障能力,本文将深度学习技术应用于场景识别中,提出了一种基于道路场景理解的巡检机器人自主避障方法(Road Scene Understanding Net,RSUNet).该方法首先将多层卷积与LeakyReLU激活函数相结合,并以残差结构和金...为提升巡检机器人的导航避障能力,本文将深度学习技术应用于场景识别中,提出了一种基于道路场景理解的巡检机器人自主避障方法(Road Scene Understanding Net,RSUNet).该方法首先将多层卷积与LeakyReLU激活函数相结合,并以残差结构和金字塔上采样结构的方式,构建高精度道路场景理解网络;其次,设计自适应控制模块来对比前后两帧图像的深层特征信息,并根据特征差异大小自动控制后续网络层的特征计算,避免相似特征重复提取,保障网络效率;最后,将场景理解结果转化为巡检机器人前方不同区域的目标信息,通过分析机器人前方可行道路区域以及障碍物所处位置来指导巡检机器人实现导航避障.实验结果表明,所提方法有效的平衡了场景理解网络的识别精度与计算效率,同时,在实际变电站巡检机器人平台上,该方法也表现出较强的适应性,并能准确高效的为机器人提供场景信息,辅助机器人完成实时自主避障.展开更多
Interactive model analysis,the process of understanding,diagnosing,and refining a machine learning model with the help of interactive visualization,is very important for users to efficiently solve real-world artificia...Interactive model analysis,the process of understanding,diagnosing,and refining a machine learning model with the help of interactive visualization,is very important for users to efficiently solve real-world artificial intelligence and data mining problems.Dramatic advances in big data analytics have led to a wide variety of interactive model analysis tasks.In this paper,we present a comprehensive analysis and interpretation of this rapidly developing area.Specifically,we classify the relevant work into three categories:understanding,diagnosis,and refinement.Each category is exemplified by recent influential work.Possible future research opportunities are also explored and discussed.展开更多
文摘为提升巡检机器人的导航避障能力,本文将深度学习技术应用于场景识别中,提出了一种基于道路场景理解的巡检机器人自主避障方法(Road Scene Understanding Net,RSUNet).该方法首先将多层卷积与LeakyReLU激活函数相结合,并以残差结构和金字塔上采样结构的方式,构建高精度道路场景理解网络;其次,设计自适应控制模块来对比前后两帧图像的深层特征信息,并根据特征差异大小自动控制后续网络层的特征计算,避免相似特征重复提取,保障网络效率;最后,将场景理解结果转化为巡检机器人前方不同区域的目标信息,通过分析机器人前方可行道路区域以及障碍物所处位置来指导巡检机器人实现导航避障.实验结果表明,所提方法有效的平衡了场景理解网络的识别精度与计算效率,同时,在实际变电站巡检机器人平台上,该方法也表现出较强的适应性,并能准确高效的为机器人提供场景信息,辅助机器人完成实时自主避障.
文摘Interactive model analysis,the process of understanding,diagnosing,and refining a machine learning model with the help of interactive visualization,is very important for users to efficiently solve real-world artificial intelligence and data mining problems.Dramatic advances in big data analytics have led to a wide variety of interactive model analysis tasks.In this paper,we present a comprehensive analysis and interpretation of this rapidly developing area.Specifically,we classify the relevant work into three categories:understanding,diagnosis,and refinement.Each category is exemplified by recent influential work.Possible future research opportunities are also explored and discussed.