论文标题
部分可观测时空混沌系统的无模型预测
Multi-level and multi-modal feature fusion for accurate 3D object detection in Connected and Automated Vehicles
论文作者
论文摘要
为了针对连接和自动化车辆（CAV）的高度准确的对象检测，本文提出了一个基于深神网络的3D对象检测模型，该模型通过开发一种新型的LiDAR-Camera融合方案来利用三阶段的特征提取器。提出的特征提取器从两种输入感觉方式中提取了高级特征，并恢复了在卷积过程中丢弃的重要特征。新颖的融合方案有效地融合了跨感觉方式和卷积层的特征，以找到最佳的代表性全球特征。融合功能由两个阶段网络共享：区域提案网络（RPN）和检测头（DH）。 RPN产生高核心建议，DH产生最终检测结果。实验结果表明，所提出的模型优于Kitti 2D和3D检测基准的最新研究，特别是对于遥远和高度遮挡的实例。
Aiming at highly accurate object detection for connected and automated vehicles (CAVs), this paper presents a Deep Neural Network based 3D object detection model that leverages a three-stage feature extractor by developing a novel LIDAR-Camera fusion scheme. The proposed feature extractor extracts high-level features from two input sensory modalities and recovers the important features discarded during the convolutional process. The novel fusion scheme effectively fuses features across sensory modalities and convolutional layers to find the best representative global features. The fused features are shared by a two-stage network: the region proposal network (RPN) and the detection head (DH). The RPN generates high-recall proposals, and the DH produces final detection results. The experimental results show the proposed model outperforms more recent research on the KITTI 2D and 3D detection benchmark, particularly for distant and highly occluded instances.
