Open AccessA Two-Stage Detection Method of Rigid Pantograph Catenary Contact Points Using DCNNS
Author(s) -
Danyang Zheng,
Xuemin Lu,
Wei Quan,
Yuchen Peng,
Yueping Liu,
Jim X. Chen
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1754/1/012071
Subject(s) - catenary , pantograph , computer science , point (geometry) , contact force , artificial intelligence , convolutional neural network , computer vision , engineering , geometry , structural engineering , engineering drawing , mathematics , physics , quantum mechanics
Pantograph catenary contact point is an important monitoring object during pantograph catenary operation, which reflects the state of pantograph catenary operation. However, due to the relatively small contact area of the target area, it is still a challenge to locate the contact point quickly and accurately. Therefore, we propose a two-stage detection method of rigid pantograph catenary contact points based on deep convolution neural network. Firstly, yolov3 network is used to locate the pantograph catenary contact part, which can obtain the target area including contact points. Then, four key points generated by the intersection of rigid pantograph and catenary can be obtained by using the key point detection network in the target area. Finally, the positioning of pantograph catenary contact points is obtained by geometric calculation. The experimental results on the railway operation data set collected by the traction Laboratory of Southwest Jiaotong University show the effectiveness of the method.