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Wang Guiping
Researcher at Chang'an University
Publications - 10
Citations - 114
Wang Guiping is an academic researcher from Chang'an University. The author has contributed to research in topics: Pixel & Line (geometry). The author has an hindex of 4, co-authored 10 publications receiving 60 citations.
Papers
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Journal ArticleDOI
Lane Detection of Curving Road for Structural Highway With Straight-Curve Model on Vision
TL;DR: Experiments show that this curve detection algorithm can accurately identify the curve lane-line, provide effective traffic information, make early warning, and it also has certain universality.
Journal ArticleDOI
Measurement for cracks at the bottom of bridges based on tethered creeping unmanned aerial vehicle
TL;DR: A high-precision unlimited endurance detection plan based on the tethered creeping UAV is designed to use for the bottom cracks of the bridge structure, and it turned out that they are practicable/applicable in various crack images of different shapes.
Journal ArticleDOI
A novel power stability drive system of semiconductor Laser Diode for high-precision measurement:
TL;DR: In this paper, the authors present the requirements of the current high-precision measurement system for stable output power of the semiconductor laser diode, a semiconductor LD stable power drive and multi-cluster multilevel LDA.
Patent
Unmanned aerial vehicle vision wire patrol method based on gradient constraint Radon transform
Huang He,Lei Xu,Yi Meng,Wang Guiping,Wang Ping,Xu Zhe,Zhang Tao,Guo Lu,Huang Ying,Li Yanbei,Kong Yitian,Wang Huifeng,Chen Zhiqiang,Yuan Dongliang,Hu Kaiyi +14 more
TL;DR: In this article, an unmanned aerial vehicle vision wire patrol method based on gradient constraint Radon transform was proposed for detecting power transmission line in the image, removing redundant edge information and improves accuracy of power transmission transmission line identification.
Journal ArticleDOI
Detection of HF-ERW status by neural network on imaging
TL;DR: In this article, a radial basis function neural network (RBFNN) was used for welding defect conditions with high-speed images of the joint melting phenomenon, based on principal component analysis (PCA).