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Sensors 2018, 18(3), 698; https://doi.org/10.3390/s18030698

New Keypoint Matching Method Using Local Convolutional Features for Power Transmission Line Icing Monitoring

State Key Laboratory of Virtual Reality Technology and Systems, School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
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Received: 3 January 2018 / Revised: 15 February 2018 / Accepted: 19 February 2018 / Published: 26 February 2018
(This article belongs to the Special Issue Sensors Signal Processing and Visual Computing)
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Abstract

Power transmission line icing (PTLI) problems, which cause tremendous damage to the power grids, has drawn much attention. Existing three-dimensional measurement methods based on binocular stereo vision was recently introduced to measure the ice thickness in PTLI, but failed to meet requirements of practical applications due to inefficient keypoint matching in the complex PTLI scene. In this paper, a new keypoint matching method is proposed based on the local multi-layer convolutional neural network (CNN) features, termed Local Convolutional Features (LCFs). LCFs are deployed to extract more discriminative features than the conventional CNNs. Particularly in LCFs, a multi-layer features fusion scheme is exploited to boost the matching performance. Together with a location constraint method, the correspondence of neighboring keypoints is further refined. Our approach achieves 1.5%, 5.3%, 13.1%, 27.3% improvement in the average matching precision compared with SIFT, SURF, ORB and MatchNet on the public Middlebury dataset, and the measurement accuracy of ice thickness can reach 90.9% compared with manual measurement on the collected PTLI dataset. View Full-Text
Keywords: power transmission line icing; keypoint matching; convolutional neural network; feature fusion; location constraint power transmission line icing; keypoint matching; convolutional neural network; feature fusion; location constraint
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Guo, Q.; Xiao, J.; Hu, X. New Keypoint Matching Method Using Local Convolutional Features for Power Transmission Line Icing Monitoring. Sensors 2018, 18, 698.

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