Name
Abstract | ||
|---|---|---|
The Hough Transform (HT), the Radon Transform (RT) and the Line Segment Detector (LSD) are the most well-known methods for line detection. But the HT and RT methods cost large computing consumption and always resulting a poor performance with many outliers. The LSD method is not effective to the real application of complex background condition. In this paper, a boundary search radon transform (BSRT) approach is proposed for high voltage transmission line detection in Unmanned Aerial Vehicle (UAV) based routing inspection. The core idea assumes that the initial point of an integral line is on one of the four image boundaries, so it isn't always necessary to analyze all points in an image and a new line detection strategy is designed. Comparing to the HT, RT and LSD methods, our method is validated by the experimental results to be fast, efficient and reliable to complex environments. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/AIM.2013.6584150 | AIM |
Keywords | Field | DocType |
power transmission lines,rt method,boundary search radon transform approach,integral line,ht method,line segment detector,complex background condition,bsrt approach,mobile robots,image boundaries,autonomous aerial vehicles,automatic optical inspection,uav-based routing inspection,lsd method,high voltage transmission line detection,unmanned aerial vehicle-based routing inspection,radon transforms,hough transform,telerobotics,hough transforms,new line detection strategy,robot vision,algorithm design and analysis,image segmentation,inspection,approximation algorithms | Computer vision,Transmission line,Computer science,Simulation,Outlier,Hough transform,Electric power transmission,Artificial intelligence,Telerobotics,Radon transform,Detector,Mobile robot | Conference |
Volume | Issue | ISSN |
null | null | 2159-6247 |
ISBN | Citations | PageRank |
978-1-4673-5319-9 | 3 | 0.39 |
References | Authors | |
13 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weiran Cao | 1 | 5 | 1.10 |
| Linlin Zhu | 2 | 28 | 5.98 |
| Jianda Han | 3 | 220 | 60.61 |
| Tianran Wang | 4 | 5 | 1.10 |
| Yingkui Du | 5 | 17 | 7.23 |