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

Sensors
Qiangliang GuoXiaoguang Hu

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.

References

Oct 22, 2005·IEEE Transactions on Pattern Analysis and Machine Intelligence·Krystian Mikolajczyk, Cordelia Schmid
May 9, 2014·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society· Jiayi Ma Zhuowen Tu
Mar 18, 2015·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Dong LiKin-Man Lam
Aug 16, 2015·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Jiayi MaAlan L Yuille
Jun 24, 2017·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Baochang ZhangLing Shao
Jul 11, 2018·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society· Baochang Zhang Ling Shao

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Methods Mentioned

BETA
feature extraction
Feature Fusion

Software Mentioned

FAST
MatchNet

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