摘要
针对低光照、雨雾等恶劣场景对智能驾驶视觉系统检测能力的影响,提出了一种雷达与相机特征融合的网络模型.基于毫米波雷达信息和注意力模型构建了雷达注意力机制特征模块,该模块可以为特征融合网络提供一个先验信息和增加算法在目标候选区域权重.测试结果表明,引入雷达注意力机制模块后,特征融合网络的目标检测性能要比仅依赖计算机视觉的检测性能有了明显的提升,并且在复杂场景下的目标检测鲁棒性更强.
A network model for radar and camera feature fusion was proposed to deal with the impact of low light,rain and fog and other harsh scenes on the detection capability of intelligent driving vision systems.A radar attention mechanism feature module was constructed based on millimeter wave radar information and attention model to provide a priori information and increase the weight of the algorithm in the target candidate region for the feature fusion network.The test results show that,introducing the radar attention mechanism module,the target detection performance of the feature fusion network is significantly better than the detection performance of that relying on computer vision alone,and the target detection is more robust in complex scenes.
作者
常亮
白傑
黄李波
CHANG Liang;BAI Jie;HUANG Libo(School of Automotive Studies,Tongji University,Shanghai 200082,China;Innovation Academy for Microsatellites of CAS,Shanghai 201210,China)
出处
《北京理工大学学报》
EI
CAS
CSCD
北大核心
2022年第3期318-323,共6页
Transactions of Beijing Institute of Technology
基金
上海市扬帆计划(19TF1446200)。
关键词
传感器融合
多目标检测
注意力机制
卷积神经网络
sensor fusion
multi-target detection
attention mechanisms
convolutional neural networks
