Pole-Like Road Object Detection in Mobile LiDAR Data via Supervoxel and Bag-of-Contextual-Visual-Words Representation
Pole-Like Road Object Detection in Mobile LiDAR Data via Supervoxel and Bag-of-Contextual-Visual-Words Representation
复制标题
通过超级体素和上下文视觉词袋表示在移动 LiDAR 数据中检测杆状道路物体
DOI：
10.1109/lgrs.2016.2521684
复制
发表时间：
2016-04-01
影响因子：
4.8
通讯作者：
Liu, Pengfei
中科院分区：
文献类型：
--
作者：
Guan, Haiyan;Yu, Yongtao;Liu, Pengfei
这封信解决了从移动的光探测和测距（LiDAR）数据中检测交通相关应用中的杆状道路物体（包括灯杆和交通路标）的问题。该方法包括两个连续的阶段：训练和杆状物体检测。在训练阶段，上下文视觉词汇是从训练数据集生成的超体素分割的特征区域创建的。在杆状对象检测阶段，为从移动的LiDAR数据分割的每个语义对象生成上下文视觉词袋表示。实验结果表明，该方法实现了88.9%，11.1%和2.8%，分别在检测杆状道路对象的正确性，遗漏和佣金。计算复杂度分析表明，该方法为从大量移动的LiDAR数据中快速准确地检测杆状物体提供了一种有前途的有效解决方案。
This letter addresses the problem of detecting pole-like road objects (including light poles and traffic signposts) from mobile light detection and ranging (LiDAR) data for transportation-related applications. The method consists of two consecutive stages: training and pole-like object detection. At the training stage, a contextual visual vocabulary is created from the feature regions generated from a training data set by supervoxel segmentation. At the pole-like object detection stage, a bag-of-contextual-visual-words representation is generated for each semantic object segmented from mobile LiDAR data. The experimental results show that the proposed method achieves correctness, omission, and commission of 88.9%, 11.1%, and 2.8%, respectively, in detecting pole-like road objects. Computational complexity analysis demonstrates that our method provides a promising and effective solution to rapid and accurate detection of pole-like objects from large volumes of mobile LiDAR data.