.png){kind=icon}
Return
ODSPC: deep learning-based 3D object detection using semantic point cloud
.png){kind=icon}
PRE
AI
DOI：10.1007/s00371-023-02820-2.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Three-dimensional object detection plays a key role in autonomous driving, which becomes extremely challenging in occlusion situations. This paper presents a novel multimodal 3D object detection framework which fuses visual semantic information and depth point cloud information to accurately detect targets with distant object features and occlusion situations. The framework consists of the four steps. Firstly, an improved semantic segmentation network is used to extract semantic information of objects containing similar features. Secondly, semantic images and point clouds are combined to generate pixel-level fusion data so that the semantic information and training capability of sparse and far-point clouds can be improved. Thirdly, a deep learning-based point cloud classification network is used for training of the fused data to output accurate detection frames. Fourthly, an extended Kalman filter is incorporated into point cloud prediction for image-based object detection to further enhance the robustness of object detection. Both Cityscapes and KITTI datasets are used in ablation study and experiments to validate the effectiveness of the proposed framework.
Keywords:
Object detection
Semantic segmentation
Point cloud classification
Fused data
Extended Kalman filter
Journal
IF:
2.9
Papers:
4.6K
Citations:
6.5K
Organization
Cited Papers
Maximizing the Computational Efficiency of Temporary Housing Decision Support Following Disasters
PREAI
ShareSaveShareSaveReporting bias in the literature on the associations of health-related behaviors and statins with cardiovascular disease and all-cause mortality.png)
PLOS Biology
OAAI
PLOS BiologyIF0
OAAIShareSaveShareSaveShareSave
View more