{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T03:12:17Z","timestamp":1776309137700,"version":"3.50.1"},"reference-count":57,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2019,10,1]],"date-time":"2019-10-01T00:00:00Z","timestamp":1569888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2019,10,1]],"date-time":"2019-10-01T00:00:00Z","timestamp":1569888000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,10,1]],"date-time":"2019-10-01T00:00:00Z","timestamp":1569888000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Eskişehir Technical University Scientific Research Project Commission","award":["1508F598"],"award-info":[{"award-number":["1508F598"]}]},{"name":"Eskişehir Technical University Scientific Research Project Commission","award":["1608F606"],"award-info":[{"award-number":["1608F606"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Aerosp. Electron. Syst."],"published-print":{"date-parts":[[2019,10]]},"DOI":"10.1109\/taes.2018.2883879","type":"journal-article","created":{"date-parts":[[2018,11,28]],"date-time":"2018-11-28T20:24:45Z","timestamp":1543436685000},"page":"2241-2252","source":"Crossref","is-referenced-by-count":62,"title":["Power Line Recognition From Aerial Images With Deep Learning"],"prefix":"10.1109","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6782-0431","authenticated-orcid":false,"given":"Omer Emre","family":"Yetgin","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1586-9302","authenticated-orcid":false,"given":"Burak","family":"Benligiray","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8183-1356","authenticated-orcid":false,"given":"Omer Nezih","family":"Gerek","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.3390\/rs71114680"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2015.7301382"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2014.131"},{"key":"ref31","first-page":"647","article-title":"DeCAF: A deep convolutional activation feature for generic visual recognition","author":"donahue","year":"2014","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2016.2526603"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/258619"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298935"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref27","first-page":"1","article-title":"Visual categorization with bags of keypoints","author":"csurka","year":"2004","journal-title":"Proc Workshop Statist Learn Comput Vis Eur Conf Comput Vis"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref2","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref1","article-title":"Pilot's aid for detecting power lines","author":"greene","year":"1999"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TAES.2009.5259175"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.5194\/isprsarchives-XXXIX-B3-491-2012"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/s00138-009-0206-y"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2013.09.023"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/GlobalSIP.2016.7905967"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1989.1.4.541"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TAES.2014.120732"},{"key":"ref50","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","author":"glorot","year":"2010","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref51","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","volume":"abs 1704 4861","author":"howard","year":"2017","journal-title":"CoRR"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2535302"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.1979.4310076"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/SIU.2018.8404196"},{"key":"ref54","first-page":"1","article-title":"Striving for simplicity: The all convolutional net","author":"springenberg","year":"2015","journal-title":"Proc Workshop Int Conf Learn Represent"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1145\/2684746.2689060"},{"key":"ref52","article-title":"Deep compression: Compressing deep neural network with pruning, trained quantization and Huffman coding","volume":"abs 1510 149","author":"han","year":"2015","journal-title":"CoRR"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2011.2155079"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ELECO.2015.7394489"},{"key":"ref40","article-title":"Ground Truth of Powerline Dataset (Infrared-IR and Visible Light-VL)","author":"yetgin","year":"2017"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/SIU.2013.6531237"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/361237.361242"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2008.300"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2011.06.001"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/IDAP.2017.8090290"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/IDAP.2017.8090291"},{"key":"ref18","first-page":"102","article-title":"Automatic recognition of scenes with power line wires in real life aerial images using DCT-based features","author":"yetgin","year":"2017","journal-title":"Digit Signal Process"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2007.895714"},{"key":"ref4","first-page":"818","article-title":"Visualizing and understanding convolutional networks","author":"zeiler","year":"2014","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.50"},{"key":"ref5","article-title":"Very deep convolutional networks for large-scale image recognition","volume":"abs 1409 1556","author":"simonyan","year":"2014","journal-title":"CoRR"},{"key":"ref8","first-page":"718","article-title":"LIDAR obstacle warning and avoidance system for unmanned aircraft","volume":"8","author":"sabatini","year":"2014","journal-title":"Int J Mech Aerosp Ind Mechatron Manuf Eng"},{"key":"ref7","article-title":"Powerline Image Dataset Extra (Infrared-IR and Visible Light-VL)-Classified (Easy and Hard)","author":"yetgin","year":"2017"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2009.5459469"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/7.272288"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"ref45","first-page":"338","article-title":"Estimating continuous distributions in Bayesian classifiers","author":"john","year":"1995","journal-title":"Proc Conf Uncertain Artif Intell"},{"key":"ref48","first-page":"1089","article-title":"On random weights and unsupervised feature learning","author":"saxe","year":"2011","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref47","article-title":"The WEKA Workbench. Online Appendix for ‘Data Mining: Practical Machine Learning Tools and Techniques,’ 4th ed.","author":"frank","year":"2016"},{"key":"ref42","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref41","article-title":"Keras","author":"chollet","year":"2015"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994018"},{"key":"ref43","first-page":"630","article-title":"Identity mappings in deep residual networks","author":"he","year":"2016","journal-title":"Proc Eur Conf Comput Vis"}],"container-title":["IEEE Transactions on Aerospace and Electronic Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/7\/8866758\/08550771.pdf?arnumber=8550771","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T20:52:02Z","timestamp":1657745522000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8550771\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10]]},"references-count":57,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/taes.2018.2883879","relation":{},"ISSN":["0018-9251","1557-9603","2371-9877"],"issn-type":[{"value":"0018-9251","type":"print"},{"value":"1557-9603","type":"electronic"},{"value":"2371-9877","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,10]]}}}