Circular Object Detection in Polar Coordinates for 2D LIDAR Data

2016 ◽  
pp. 65-78 ◽  
Author(s):  
Xianen Zhou ◽  
Yaonan Wang ◽  
Qing Zhu ◽  
Zhiqiang Miao
Keyword(s):  
Object Detection ◽  
Polar Coordinates ◽  
Lidar Data ◽  
Circular Object

scholarly journals Fuzzified Circular Gabor Filter for Circular and Near-Circular Object Detection

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 96706-96713 ◽  
Author(s):  
Vladimir Tadic ◽  
Akos Odry ◽  
Attila Toth ◽  
Zoltan Vizvari ◽  
Peter Odry
Keyword(s):  
Object Detection ◽  
Gabor Filter ◽  
Circular Object ◽  
Circular Gabor Filter

scholarly journals Arbitrary-Oriented Object Detection in Remote Sensing Images Based on Polar Coordinates

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 223373-223384
Author(s):  
Lin Zhou ◽  
Haoran Wei ◽  
Hao Li ◽  
Wenzhe Zhao ◽  
Yi Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Polar Coordinates ◽  
Remote Sensing Images ◽  
Oriented Object

Fast and Robust Circular Object Detection With Probabilistic Pairwise Voting

2011 ◽  
Vol 18 (11) ◽  
pp. 639-642 ◽  
Author(s):  
Lili Pan ◽  
Wen-Sheng Chu ◽  
J. M. Saragih ◽  
F. De la Torre ◽  
Mei Xie
Keyword(s):  
Object Detection ◽  
Circular Object

Deep 3D Object Detection Networks Using LiDAR Data: A Review

2021 ◽  
Vol 21 (2) ◽  
pp. 1152-1171
Author(s):  
Yutian Wu ◽  
Yueyu Wang ◽  
Shuwei Zhang ◽  
Harutoshi Ogai
Keyword(s):  
Object Detection ◽  
Lidar Data ◽  
3D Object ◽  
3D Object Detection

Fast Circular Object Detection Using Multiscale Grayscale Hough Transform

2014 ◽  
Vol 490-491 ◽  
pp. 1542-1547 ◽  
Author(s):  
Wen Xia Yang ◽  
Zhang Can Huang
Keyword(s):  
Object Detection ◽  
Hough Transform ◽  
Dramatic Improvement ◽  
Gray Level ◽  
3 Dimensional ◽  
Radial Gradient ◽  
Multi Scale ◽  
Second Stage ◽  
Circular Object ◽  
Edge Detecting

A fast Hough transform for circular object detection is proposed in this paper which can be directly applied to gray level images. This method consists of three major stages. In the first stage, the center positions of circular objects are detected using the gray level Hough transform, which requires no conventional preprocessing such as edge detecting and binarization. The second stage determines the radius of the detected objects by analyzing the radial gradient profile. In order to detect objects with different radius in the same scene, a multi-scale strategy is integrated in the proposed method. Compared with traditional Hough transform, the gray level Hough transform uses a 2-dimensional accumulation map rather than the 3-dimensional one, which results in a dramatic improvement on the computational efficiency. Experiments have been carried out on more than 2000 real-world images and the result shows that 90.3% of the circular objects have been accurately detected, which demonstrate the applicability of the proposed method.

Pole-Like Road Object Detection in Mobile LiDAR Data via Supervoxel and Bag-of-Contextual-Visual-Words Representation

2016 ◽  
Vol 13 (4) ◽  
pp. 520-524 ◽  
Author(s):  
Haiyan Guan ◽  
Yongtao Yu ◽  
Jonathan Li ◽  
Pengfei Liu
Keyword(s):  
Object Detection ◽  
Lidar Data ◽  
Visual Words

scholarly journals Automated Object Detection, Mapping, and Assessment of Roadside Clear Zones Using Lidar Data

2021 ◽  
pp. 036119812110299
Author(s):  
Maged Gouda ◽  
Bruno Arantes de Achilles Mello ◽  
Karim El-Basyouny
Keyword(s):  
Object Detection ◽  
Cost Effective ◽  
Design Guidelines ◽  
Lidar Data ◽  
Road Maintenance ◽  
Rural Highways ◽  
Crash Data ◽  
Vehicle Trajectory ◽  
Automated Object Detection ◽  
Effective Source

This paper proposes a fully automated approach to map and assess roadside clearance parameters using mobile Light Detection and Ranging (lidar) data on rural highways. Compared with traditional manual surveying methods, lidar data could provide a more efficient and cost-effective source to extract roadside information. This study proposes a novel voxel-based raycasting approach focused primarily on automating roadside mapping and assessment. First, the scanning vehicle trajectory is extracted. Pavement surface points are then detected, and a method is proposed to extract pavement edge trajectories. Once pavement edges are extracted, guardrails were identified using a conical frustum emitted from the edge trajectory points. Target points and flexion points are then generated and located on the roadside, and a voxel-based raycasting approach is used to search for roadside obstacles and query their locations. Finally, roadside slopes and embankment heights were mapped at specific intervals, and roadside design guidelines and requirements were automatically checked against the mapping results. Noncompliant locations with substandard conditions were automatically queried. The method was tested on four highway segments in Alberta, Canada. The accuracy of the edge detection reached up to 98.5%. Furthermore, the method proved to be accurate in object detection, being able to detect all obstructions on the roadside in each tested segment. The proposed method can help transportation authorities automatically map and inventory roadside clearance parameters. Moreover, the safety performance of existing road infrastructure can be studied using collected information and crash data to support decision making on road maintenance and upgrades.

Sign in / Sign up

Export Citation Format

Share Document