UFLD(ultra fast structure aware deep lane detection)是一种轻量化车道线检测模型,为提升模型的检测精度,对模型进行改进。引入CAM(channel attention mechanism)使模型能更关注携带重要车道线信息的特征通道和像素;为了感知车道线...UFLD(ultra fast structure aware deep lane detection)是一种轻量化车道线检测模型,为提升模型的检测精度,对模型进行改进。引入CAM(channel attention mechanism)使模型能更关注携带重要车道线信息的特征通道和像素;为了感知车道线的细节信息,引入ASPP(atrous spatial pyramid pooling)扩大卷积过程的感受野,提高模型分割精度;搭建引入CAM和ASPP后的改进模型,并在改进的模型上进行实验。实验结果表明:在TuSimple数据集上以ResNet18为主干网络的模型检测精度由95.81%提升至95.98%,以ResNet34为主干网络的模型检测精度由95.84%提升至96.12%;在CULane数据集上,无论是以ResNet18还是以ResNet34为主干网络模型,其平均精度均有不同程度的提高。展开更多
This paper reports on progress made in the first 3 years of.ATR's 'CAM-Brain'Project, which aims to use 'evolutionary e.gi...,i.gi' techniques to build/grow/evolve a RAM-and-cellular-automata based...This paper reports on progress made in the first 3 years of.ATR's 'CAM-Brain'Project, which aims to use 'evolutionary e.gi...,i.gi' techniques to build/grow/evolve a RAM-and-cellular-automata based artificial brain consisting of thousands of interconnected neural network modules inside special hardware such as MITs Cellular Automata Machine 'CAM-8,i, or NTT's Content Addressable Memory System 'CAM-System'. The states of a billion (later a trillion) 3D cellular automata cells, and edlions of cellular automata rules which govern their state changes, can be stored relatively cheaply in giga(tera)bytes of RAM. After 3 years work, the CA rules are almost ready. MITt,,'CAM-8' (essentially a serial device) can update 200,000,000 CA cells a second. It is possible that NTT's 'CAM-System' (essentially a massively parallel device) may be able to update a trillion CA cells a second. Hence all the ingredients will soon be ready to create a revolutionary new technology which will allow thousands of evolved neural network modules to be assembled into artificial brains. This in turn will probably create not only a new research field, but hopefully a whole new industry,namely 'brain building'. Building artificial brains with a billion neurons is the aim of ATR's 8 year i,CAM-B,ai.,' research project, ending in 2001.展开更多
The model of EQUnn (equivalent neural network of the CAM-Brain model) is proposed. With the help of EQUnn model, it is proved that the CAM-Brain can solve the XOR problem.
文摘UFLD(ultra fast structure aware deep lane detection)是一种轻量化车道线检测模型,为提升模型的检测精度,对模型进行改进。引入CAM(channel attention mechanism)使模型能更关注携带重要车道线信息的特征通道和像素;为了感知车道线的细节信息,引入ASPP(atrous spatial pyramid pooling)扩大卷积过程的感受野,提高模型分割精度;搭建引入CAM和ASPP后的改进模型,并在改进的模型上进行实验。实验结果表明:在TuSimple数据集上以ResNet18为主干网络的模型检测精度由95.81%提升至95.98%,以ResNet34为主干网络的模型检测精度由95.84%提升至96.12%;在CULane数据集上,无论是以ResNet18还是以ResNet34为主干网络模型,其平均精度均有不同程度的提高。
文摘This paper reports on progress made in the first 3 years of.ATR's 'CAM-Brain'Project, which aims to use 'evolutionary e.gi...,i.gi' techniques to build/grow/evolve a RAM-and-cellular-automata based artificial brain consisting of thousands of interconnected neural network modules inside special hardware such as MITs Cellular Automata Machine 'CAM-8,i, or NTT's Content Addressable Memory System 'CAM-System'. The states of a billion (later a trillion) 3D cellular automata cells, and edlions of cellular automata rules which govern their state changes, can be stored relatively cheaply in giga(tera)bytes of RAM. After 3 years work, the CA rules are almost ready. MITt,,'CAM-8' (essentially a serial device) can update 200,000,000 CA cells a second. It is possible that NTT's 'CAM-System' (essentially a massively parallel device) may be able to update a trillion CA cells a second. Hence all the ingredients will soon be ready to create a revolutionary new technology which will allow thousands of evolved neural network modules to be assembled into artificial brains. This in turn will probably create not only a new research field, but hopefully a whole new industry,namely 'brain building'. Building artificial brains with a billion neurons is the aim of ATR's 8 year i,CAM-B,ai.,' research project, ending in 2001.
文摘The model of EQUnn (equivalent neural network of the CAM-Brain model) is proposed. With the help of EQUnn model, it is proved that the CAM-Brain can solve the XOR problem.
