{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T23:26:05Z","timestamp":1762298765160,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2018,2,26]],"date-time":"2018-02-26T00:00:00Z","timestamp":1519603200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s18030698","type":"journal-article","created":{"date-parts":[[2018,2,27]],"date-time":"2018-02-27T04:20:47Z","timestamp":1519705247000},"page":"698","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["New Keypoint Matching Method Using Local Convolutional Features for Power Transmission Line Icing Monitoring"],"prefix":"10.3390","volume":"18","author":[{"given":"Qiangliang","family":"Guo","sequence":"first","affiliation":[{"name":"State Key Laboratory of Virtual Reality Technology and Systems, School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China"}]},{"given":"Jin","family":"Xiao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Virtual Reality Technology and Systems, School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China"}]},{"given":"Xiaoguang","family":"Hu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Virtual Reality Technology and Systems, School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,2,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.1109\/TPWRD.2014.2305980","article-title":"Predictive model for equivalent ice thickness load on overhead transmission lines based on measured insulator string deviations","volume":"29","author":"Jiang","year":"2014","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2163","DOI":"10.1109\/TPWRD.2011.2157947","article-title":"A fiber bragg grating tension and tilt sensor applied to icing monitoring on overhead transmission lines","volume":"26","author":"Ma","year":"2011","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1016\/j.engappai.2011.11.004","article-title":"Learning to predict ice accretion on electric power lines","volume":"25","author":"Zarnani","year":"2012","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1080","DOI":"10.1109\/TPWRD.2004.838518","article-title":"Statistical analysis of field data for precipitation icing accretion on overhead power lines","volume":"20","author":"Farzaneh","year":"2005","journal-title":"IEEE Trans. Power Del."},{"key":"ref_5","first-page":"563","article-title":"Application of self-adaptive segmental threshold to ice thickness identification","volume":"3","author":"Lu","year":"2009","journal-title":"High Volt. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Gu, I.Y.H., Berlijn, S., Gutman, I., and Bollen, M.H.J. (2013, January 10\u201313). Practical applications of automatic image analysis for overhead lines. Proceedings of the 22nd International Conference and Exhibition on Electricity Distribution (CIRED 2013), Stockholm, Sweden.","DOI":"10.1049\/cp.2013.0802"},{"key":"ref_7","first-page":"368","article-title":"Wavelet image recognition of ice thickness on transmission lines","volume":"40","author":"Hao","year":"2014","journal-title":"High Volt. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yu, C., Peng, Q., Wachal, R., and Wang, P. (2006, January 7\u201310). An Image-Based 3D Acquisition of Ice Accretions on Power Transmission Lines. Proceedings of the CCECE\u201906 Canadian Conference on Electrical and Computer Engineering, Ottawa, ON, Canada.","DOI":"10.1109\/CCECE.2006.277844"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wachal, R., Stoezel, J., Peckover, M., and Godkin, D. (2012, January 7\u201310). A computer vision early-warning ice detection system for the Smart Grid. Proceedings of the Transmission and Distribution Conference and Exposition, Orlando, FL, USA.","DOI":"10.1109\/TDC.2012.6281621"},{"key":"ref_10","first-page":"103","article-title":"On-line monitoring method of icing transmission lines based on 3D reconstruction","volume":"36","author":"Yang","year":"2012","journal-title":"Autom. Electr. Power Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/TIP.2015.2467217","article-title":"Non-rigid point set registration by preserving global and local structures","volume":"25","author":"Ma","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6469","DOI":"10.1109\/TGRS.2015.2441954","article-title":"Robust feature matching for remote sensing image registration via locally linear transforming","volume":"53","author":"Ma","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1115","DOI":"10.1109\/TSP.2014.2388434","article-title":"Robust L2E Estimation of Transformation for Non-Rigid Registration","volume":"63","author":"Ma","year":"2015","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1706","DOI":"10.1109\/TIP.2014.2307478","article-title":"Robust point matching via vector field consensus","volume":"23","author":"Ma","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive Image Features from Scale-Invariant Keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Bay, H., Tuytelaars, T., and Van Gool, L. (2006, January 7\u201313). SURF: Speeded Up Robust Features. Proceedings of the 9th European Conference on Computer Vision, Graz, Austria.","DOI":"10.1007\/11744023_32"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Rublee, E., Rabaud, V., Konolige, K., and Bradski, G. (2011, January 6\u201313). ORB: An efficient alternative to SIFT or SURF. Proceedings of the IEEE International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4648","DOI":"10.1109\/TIP.2017.2718189","article-title":"Action recognition using 3D histograms of texture and a multi-class boosting classifier","volume":"26","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_19","unstructured":"Fischer, P., Dosovitskiy, A., and Brox, T. (2016, June 01). Descriptor matching with convolutional neural networks: a comparison to sift. Available online: https:\/\/arxiv.org\/abs\/1405.5769."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., and Komodakis, N. (2015). Learning to compare image patches via convolutional neural networks. Comput. Vis. Pattern Recogn., 4353\u20134361.","DOI":"10.1109\/CVPR.2015.7299064"},{"key":"ref_21","unstructured":"Han, X., Leung, T., Jia, Y., Sukthankar, R., and Berg, A.C. (2015, January 7\u201312). MatchNet: Unifying feature and metric learning for patch-based matching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Simo-Serra, E., Trulls, E., Ferraz, L., Kokkinos, I., Fua, P., and Morenonoguer, F. (2015, January 7\u201313). Discriminative Learning of Deep Convolutional Feature Point Descriptors. Proceedings of the International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.22"},{"key":"ref_23","first-page":"1","article-title":"Stereo matching by training a convolutional neural network to compare image patches","volume":"17","author":"Zbontar","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Luo, W., Schwing, A.G., and Urtasun, R. (2016, January 27\u201330). Efficient Deep Learning for Stereo Matching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.614"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1016\/j.image.2016.06.002","article-title":"Robust object representation by boosting-like deep learning architecture","volume":"47","author":"Wang","year":"2016","journal-title":"Signal Process. Image Commun."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2317","DOI":"10.1109\/TIP.2015.2412374","article-title":"High-resolution face verification using pore-scale facial features","volume":"24","author":"Li","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1023\/A:1014573219977","article-title":"A taxonomy and evaluation of dense two-frame stereo correspondence algorithms","volume":"47","author":"Scharstein","year":"2002","journal-title":"Int. J. Comput. Vis."},{"key":"ref_29","unstructured":"Scharstein, D., and Szeliski, R. (2003, January 18\u201320). High-Accuracy Stereo Depth Maps Using Structured Light. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Madison, WI, USA."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Scharstein, D., and Pal, C. (2007, January 17\u201322). Learning Conditional Random Fields for Stereo. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA.","DOI":"10.1109\/CVPR.2007.383191"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Hirschmuller, H., and Scharstein, D. (2007, January 17\u201322). Evaluation of Cost Functions for Stereo Matching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA.","DOI":"10.1109\/CVPR.2007.383248"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/978-3-319-11752-2_3","article-title":"High-Resolution Stereo Datasets with Subpixel-Accurate Ground Truth","volume":"Volume 8753","author":"Jiang","year":"2014","journal-title":"Pattern Recognition"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1615","DOI":"10.1109\/TPAMI.2005.188","article-title":"A performance evaluation of local descriptors","volume":"27","author":"Mikolajczyk","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1109\/34.888718","article-title":"A flexible new technique for camera calibration","volume":"22","author":"Zhang","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1007\/s00138-009-0206-y","article-title":"Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform","volume":"21","author":"Li","year":"2010","journal-title":"Mach. Vis. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1016\/j.automatica.2013.12.035","article-title":"Cooperative and geometric learning algorithm (CGLA) for path planning of UAVs with limited information","volume":"50","author":"Zhang","year":"2014","journal-title":"Automatica"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1109\/TIP.2017.2775060","article-title":"Latent Constrained Correlation Filter","volume":"27","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Image Process."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/3\/698\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:56:31Z","timestamp":1760194591000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/3\/698"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,2,26]]},"references-count":37,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2018,3]]}},"alternative-id":["s18030698"],"URL":"https:\/\/doi.org\/10.3390\/s18030698","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2018,2,26]]}}}