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Research of Universal Vision Based Technology for High Autonomy UAVS Line Inspection
Apr 15, 2016Author:
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Research of Universal Vision Based Technology for High Autonomy UAVS Line Inspection 

  

AbstractThere are great lengths of line facilities in our country such as power lines and oil pipelines whose maintenance work are suffering from high cost and low efficiency of manual inspections. Aiming at the practical demands, in this project, research of a vision based technology for high autonomy UAVS line inspection is going to be carried out. The result technology will not be limited to a specific hardware platform or specified tasks and it will have good scalability and versatility. Focus on the three key science problems: sparse description and intelligent coding of low-flying aerial image, automatically analyzing and conclusion evaluation of vision information under complex environment, methods for generalizing intelligent technologies based on task structure analyzing, we devote to propose a complete solution by making breakthroughs in bottlenecks of intelligent coding, autonomous measurement and exception checking, solutions of generality, air-ground communication with small data. In order to achieve this goal, intelligent coding model will be established firstly based on sparse coding theory of bionic vision. On this base, computational model will be established based on the mature psychology theories to decompose an aerial photo into a series of cognitive objects for object recognition and following tasks including exception identification, pose estimation and control etc. Besides, the designing of intelligent technology’s generality will be theoretical height through structured analysis of the tasks. The applicant and the team have good research foundation and conditions and we have the ability to finish the project within three years of research task. 

  

Keywords: autonomous inspection of UAVS; image processing; scene description; object recognition; universality 

  

Contact: 

LIU Xilong 

E-mail: xilong.liu@ia.ac.cn 

Research Institute of Precise Perception and Intelligent Control