计算机科学
点云
人工智能
目标检测
特征提取
联营
图形
模式识别(心理学)
计算机视觉
理论计算机科学
作者
Yangyang Yi,Long Yu,Shengwei Tian,Xiaodong Gao,Jie Liu,Xingang Zhao
出处
期刊:Journal of Intelligent and Fuzzy Systems
[IOS Press]
日期:2024-02-14
卷期号:46 (2): 5175-5189
摘要
In recent years, 3D object detection based on LiDAR point clouds is a key component of autonomous driving. In pursuit of enhancing the accuracy of 3D point cloud feature extraction and point cloud detection, this paper introduces a novel 3D object detection model, termed as Graph Self-Attention-RCNN (GA-RCNN). This model is designed to integrate voxel information and point location information, enhancing the quality of 3D object proposals while maintaining contextual accuracy. The first stage rectifies the previous approach that relied on local features for preselected boxes, overlooking crucial global contextual information. An improved method is suggested in this work, utilizing BEV to capture long-range dependencies via a cross-attention mechanism. The second stage addresses the overreliance on local neighborhood point feature extraction. The Graph Self-Attention Pooling method is proposed, characterized by its dynamic computation of contribution weights for inputs. This enhances the model’s flexibility and generalization performance. Extensive evaluations on KITTI and Waymo datasets demonstrate GA-RCNN’s superior accuracy compared to other methods, affirming its efficacy in 3D object detection.
科研通智能强力驱动
Strongly Powered by AbleSci AI