Han Jun, Zhang Jingjing, Wang Binhai. Method on recognizing the structure of transmission line based on perceptual organization[J]. Infrared and Laser Engineering, 2013, 42(12): 3458-3463.
| Citation:
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Han Jun, Zhang Jingjing, Wang Binhai. Method on recognizing the structure of transmission line based on perceptual organization[J]. Infrared and Laser Engineering, 2013, 42(12): 3458-3463.
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Method on recognizing the structure of transmission line based on perceptual organization
- 1.
School of Communication and Information Engineering,Shanghai University,Shanghai 20072,in China;
- 2.
Electric Power Robotics Laboratory,State Grid,Shandong,Jinan 250002,in China
- Received Date: 2010-10-05
- Rev Recd Date:
2010-12-03
- Publish Date:
2013-12-25
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Abstract
In order to improve the diagnosis accuracy of the transmission line defect, and reduce the influence on identifying the structure of the transmission line made by complicated background texture and light. Starting from Gestalt perception theory, a multiple perceptual identification method was developed to identify transmission line structure. Firstly, line with different directions and different width was extracted and sorted. Through a kind of multilevel searching algorithm with similarity, continuity and colinearity of Gestalt Law, the contour of transmission line was obtained accurately and completely. Secondly, a method based on block partition was developed, which can visually perceive near parallel lines and near symmetrical cross structure. A three level classifier for clustered parallel line group was designed. Lastly, combined with prior knowledge of the transmission line model, constraint mechanism was built to recognize the structure of transmission lines, and then uniquely identify the semantically structure of transmission lines. Experimental results show that the method can effectively identify the transmission line consisting of the tower, conductor, earth wire and the insulator region through recognition of the UAV inspection acquisition transmission line image.
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