针对自动驾驶路面上目标漏检和错检的问题,提出一种基于改进Centerfusion的自动驾驶3D目标检测模型。该模型通过将相机信息和雷达特征融合,构成多通道特征数据输入,从而增强目标检测网络的鲁棒性,减少漏检问题;为了能够得到更加准确丰富...针对自动驾驶路面上目标漏检和错检的问题,提出一种基于改进Centerfusion的自动驾驶3D目标检测模型。该模型通过将相机信息和雷达特征融合,构成多通道特征数据输入,从而增强目标检测网络的鲁棒性,减少漏检问题;为了能够得到更加准确丰富的3D目标检测信息,引入了改进的注意力机制,用于增强视锥网格中的雷达点云和视觉信息融合;使用改进的损失函数优化边框预测的准确度。在Nuscenes数据集上进行模型验证和对比,实验结果表明,相较于传统的Centerfusion模型,提出的模型平均检测精度均值(mean Average Precision,mAP)提高了1.3%,Nuscenes检测分数(Nuscenes Detection Scores,NDS)提高了1.2%。展开更多
The gene of nonstructural protein 9 of SARS-coronavirus(SARS-CoV) was amplified using PCR from the product derivated from reverse transcription of SARS-CoV genome RNA,and was inserted into the multiple cloning sites o...The gene of nonstructural protein 9 of SARS-coronavirus(SARS-CoV) was amplified using PCR from the product derivated from reverse transcription of SARS-CoV genome RNA,and was inserted into the multiple cloning sites of the expression vector pGEX-6p-1.Recombinant strain induced with IPTG expressed the specific soluble protein.The nsp9 protein was harvested and purified by affinity chromatography and processed by prescission protease.Polyclonal serum against the nsp9 protein was raised in rabbit.展开更多
文摘针对自动驾驶路面上目标漏检和错检的问题,提出一种基于改进Centerfusion的自动驾驶3D目标检测模型。该模型通过将相机信息和雷达特征融合,构成多通道特征数据输入,从而增强目标检测网络的鲁棒性,减少漏检问题;为了能够得到更加准确丰富的3D目标检测信息,引入了改进的注意力机制,用于增强视锥网格中的雷达点云和视觉信息融合;使用改进的损失函数优化边框预测的准确度。在Nuscenes数据集上进行模型验证和对比,实验结果表明,相较于传统的Centerfusion模型,提出的模型平均检测精度均值(mean Average Precision,mAP)提高了1.3%,Nuscenes检测分数(Nuscenes Detection Scores,NDS)提高了1.2%。
文摘The gene of nonstructural protein 9 of SARS-coronavirus(SARS-CoV) was amplified using PCR from the product derivated from reverse transcription of SARS-CoV genome RNA,and was inserted into the multiple cloning sites of the expression vector pGEX-6p-1.Recombinant strain induced with IPTG expressed the specific soluble protein.The nsp9 protein was harvested and purified by affinity chromatography and processed by prescission protease.Polyclonal serum against the nsp9 protein was raised in rabbit.
