{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:21:15Z","timestamp":1740108075910,"version":"3.37.3"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2017,10,13]],"date-time":"2017-10-13T00:00:00Z","timestamp":1507852800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61602484","61472423"],"award-info":[{"award-number":["61602484","61472423"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61432008","U1636220"],"award-info":[{"award-number":["61432008","U1636220"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2019,7]]},"DOI":"10.1007\/s00521-017-3229-8","type":"journal-article","created":{"date-parts":[[2017,10,13]],"date-time":"2017-10-13T13:47:49Z","timestamp":1507902469000},"page":"2781-2794","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Locator slope calculation via deep representations based on monocular vision"],"prefix":"10.1007","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1895-7906","authenticated-orcid":false,"given":"Yang","family":"Yang","sequence":"first","affiliation":[]},{"given":"Wensheng","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Zewen","family":"He","sequence":"additional","affiliation":[]},{"given":"Dongjie","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,10,13]]},"reference":[{"key":"3229_CR1","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.measurement.2015.07.022","volume":"74","author":"I Aydin","year":"2015","unstructured":"Aydin I (2015) A new approach based on firefly algorithm for vision-based railway overhead inspection system. Measurement 74:43\u201355","journal-title":"Measurement"},{"issue":"C","key":"3229_CR2","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1016\/j.patcog.2015.09.010","volume":"51","author":"X Wu","year":"2016","unstructured":"Wu X, Yuan P, Peng Q et al (2016) Detection of bird nests in overhead catenary system images for high-speed rail. Pattern Recognit 51(C):242\u2013254","journal-title":"Pattern Recognit"},{"issue":"2","key":"3229_CR3","doi-asserted-by":"publisher","first-page":"938","DOI":"10.1016\/j.eswa.2014.08.026","volume":"42","author":"I Aydin","year":"2015","unstructured":"Aydin I, Karakose M, Akin E (2015) Anomaly detection using a modified kernel-based tracking in the pantograph\u2013catenary system. Expert Syst Appl 42(2):938\u2013948","journal-title":"Expert Syst Appl"},{"key":"3229_CR4","unstructured":"Aydin I, Karakose M, Akin E (2013) A robust anomaly detection in pantograph\u2013catenary system based on mean-shift tracking and foreground detection. In: IEEE international conference on systems, man, and cybernetics. SMC 2013, pp 4444\u20134449"},{"key":"3229_CR5","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.measurement.2016.11.008","volume":"97","author":"J Skibicki","year":"2017","unstructured":"Skibicki J, Bartomiejczyk M (2017) Analysis of measurement uncertainty for contact-less method used to measure the position of catenary contact wire, performed with the use of Monte Carlo method. Measurement 97:203\u2013217","journal-title":"Measurement"},{"issue":"12","key":"3229_CR6","first-page":"101","volume":"30","author":"H Fan","year":"2010","unstructured":"Fan H, Bian C, Zhu T et al (2010) Automatic Detection Technology for Gradient of Locator in Contactless Overhead Contact System [J]. Computer Applications 30(12):101\u2013103","journal-title":"Computer Applications"},{"issue":"3","key":"3229_CR7","first-page":"9","volume":"5","author":"X Wang","year":"2014","unstructured":"Wang X, Wu J, Xu K et al (2014) Identification of OCS locator based on AdaBoost algorithm. High Speed Railw Technol 5(3):9\u201312","journal-title":"High Speed Railw Technol"},{"issue":"4","key":"3229_CR8","first-page":"82","volume":"32","author":"R Duan","year":"2011","unstructured":"Duan R, Wei Z, Huang S et al (2011) Automatic measurement method of the catenary localizer slope based on computer vision. Zhongguo Tiedao Kexue China Railw Sci 32(4):82\u201387","journal-title":"Zhongguo Tiedao Kexue China Railw Sci"},{"issue":"1","key":"3229_CR9","first-page":"105","volume":"2013","author":"T Zhang","year":"2013","unstructured":"Zhang T, Hao K, Duan R (2013) Dynamic detection algorithm of slope gradient of catenary locator based on computer vision technology. Railw Stand Des 2013(1):105\u2013108","journal-title":"Railw Stand Des"},{"issue":"6","key":"3229_CR10","first-page":"1478","volume":"2015","author":"C Zhang","year":"2015","unstructured":"Zhang C, Wang S, Zhang D et al (2015) Detection of catenary steady clamp based on image processing. J Railw Sci Eng 2015(6):1478\u20131484","journal-title":"J Railw Sci Eng"},{"key":"3229_CR11","volume-title":"Research on contenary steady arm recognition method based on video image","author":"H Gu","year":"2014","unstructured":"Gu H (2014) Research on contenary steady arm recognition method based on video image. Southwest Jiaotong University, Chengdu"},{"key":"3229_CR12","doi-asserted-by":"crossref","unstructured":"Liu Y, Hou M, Rao X et al (2008) A steady corner detection of gray level images based on improved Harris algorithm. In: IEEE international conference on networking, sensing and control, pp 708\u2013713","DOI":"10.1109\/ICNSC.2008.4525308"},{"key":"3229_CR13","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems. NIPS 2012, pp 1097\u20131105"},{"key":"3229_CR14","unstructured":"Simonyan K, Zisserman A (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint \n                    arXiv:1409.1556"},{"issue":"3","key":"3229_CR15","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1007\/s00521-015-2089-3","volume":"28","author":"VB Semwal","year":"2017","unstructured":"Semwal VB, Mondal K, Nandi GC (2017) Robust and accurate feature selection for humanoid push recovery and classification: deep learning approach. Neural Comput Appl 28(3):565\u2013574","journal-title":"Neural Comput Appl"},{"issue":"4","key":"3229_CR16","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1007\/s00521-015-1916-x","volume":"27","author":"L Chen","year":"2016","unstructured":"Chen L, Qu H, Zhao J et al (2016) Efficient and robust deep learning with correntropy-induced loss function. Neural Comput Appl 27(4):1019\u20131031","journal-title":"Neural Comput Appl"},{"issue":"2","key":"3229_CR17","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1109\/TII.2016.2605629","volume":"13","author":"H Zhang","year":"2017","unstructured":"Zhang H, Cao X, Ho JKL et al (2017) Object-level video advertising: an optimization framework. IEEE Trans Ind Inf 13(2):520\u2013531","journal-title":"IEEE Trans Ind Inf"},{"issue":"2","key":"3229_CR18","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1109\/TII.2016.2601521","volume":"13","author":"H Zhang","year":"2017","unstructured":"Zhang H, Li J, Ji Y et al (2017) Understanding subtitles by character-level sequence-to-sequence learning. IEEE Trans Ind Inf 13(2):616\u2013624","journal-title":"IEEE Trans Ind Inf"},{"issue":"1","key":"3229_CR19","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1109\/TPAMI.2015.2437384","volume":"38","author":"R Girshick","year":"2016","unstructured":"Girshick R, Donahue J, Darrell T et al (2016) Region-based convolutional networks for accurate object detection and segmentation. IEEE Trans Pattern Anal Mach Intell 38(1):142\u2013158","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3229_CR20","unstructured":"Ren S, He K, Girshick R et al (2015) Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems. NIPS 2015, pp 91\u201399"},{"key":"3229_CR21","unstructured":"Redmon J, Divvala S, Girshick R et al (2015) You only look once: unified, real-time object detection. arXiv preprint \n                    arXiv:1506.02640"},{"issue":"9","key":"3229_CR22","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"K He","year":"2015","unstructured":"He K, Zhang X, Ren S et al (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37(9):1904\u20131916","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3229_CR23","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-017-2870-6","author":"N Mu","year":"2017","unstructured":"Mu N, Xu X, Zhang X et al (2017) Salient object detection using a covariance-based CNN model in low-contrast images. Neural Comput Appl. doi:\n                    10.1007\/s00521-017-2870-6","journal-title":"Neural Comput Appl"},{"issue":"2","key":"3229_CR24","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1007\/s00521-015-1846-7","volume":"27","author":"L Bai","year":"2016","unstructured":"Bai L, Li K, Pei J et al (2016) Main objects interaction activity recognition in real images. Neural Comput Appl 27(2):335\u2013348","journal-title":"Neural Comput Appl"},{"issue":"9","key":"3229_CR25","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","volume":"32","author":"PF Felzenszwalb","year":"2010","unstructured":"Felzenszwalb PF, Girshick RB, McAllester D et al (2010) Object detection with discriminatively trained part-based models. IEEE Trans Pattern Anal Mach Intell 32(9):1627\u20131645","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"3229_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11263-015-0823-z","volume":"116","author":"M Jaderberg","year":"2016","unstructured":"Jaderberg M, Simonyan K, Vedaldi A et al (2016) Reading Text in the Wild with Convolutional Neural Networks[J]. Int J Comput Vision 116(1):1\u201320","journal-title":"Int J Comput Vision"},{"issue":"1","key":"3229_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/0600000001","volume":"1","author":"V Lepetit","year":"2005","unstructured":"Lepetit V, Fua P (2005) Monocular model-based 3D tracking of rigid objects: a survey. Found Trends Comput Gr Vis 1(1):1\u201389","journal-title":"Found Trends Comput Gr Vis"},{"issue":"2","key":"3229_CR28","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1109\/TPAMI.2008.214","volume":"31","author":"D Douxchamps","year":"2009","unstructured":"Douxchamps D, Chihara K (2009) High-accuracy and robust localization of large control markers for geometric camera calibration. IEEE Trans Pattern Anal Mach Intell 31(2):376\u201383","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3229_CR29","volume-title":"Research on monocular vision based metrology for 3D rigid object","author":"D Leng","year":"2011","unstructured":"Leng D (2011) Research on monocular vision based metrology for 3D rigid object. Tsinghua University, Beijing"},{"issue":"3","key":"3229_CR30","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1007\/s11263-006-0023-y","volume":"74","author":"E Royer","year":"2007","unstructured":"Royer E, Lhuillier M, Dhome M et al (2007) Monocular Vision for Mobile Robot Localization and Autonomous Navigation[J]. Int J Comput Vision 74(3):237\u2013260","journal-title":"Int J Comput Vision"},{"key":"3229_CR31","first-page":"593","volume":"2005","author":"J Michels","year":"2005","unstructured":"Michels J, Saxena A, Ng AY (2005) High speed obstacle avoidance using monocular vision and reinforcement learning. Int Conf DBLP 2005:593\u2013600","journal-title":"Int Conf DBLP"},{"issue":"10","key":"3229_CR32","doi-asserted-by":"publisher","first-page":"3056","DOI":"10.1109\/ICRA.2011.5980343","volume":"47","author":"M Achtelik","year":"2011","unstructured":"Achtelik M, Achtelik M, Weiss S et al (2011) Onboard IMU and monocular vision based control for MAVs in unknown in and outdoor environments[C]. IEEE International Conference on Robotics and Automation 47(10):3056\u20133063","journal-title":"IEEE International Conference on Robotics and Automation"},{"issue":"2","key":"3229_CR33","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1007\/s11263-013-0620-5","volume":"104","author":"JRR Uijlings","year":"2013","unstructured":"Uijlings JRR, van de Sande KEA, Gevers T et al (2013) Selective search for object recognition. Int J Comput Vis 104(2):154\u2013171","journal-title":"Int J Comput Vis"},{"key":"3229_CR34","unstructured":"Zitnick CL, Dollr P (2014) Edge boxes: locating object proposals from edges. In: European conference on computer vision, ECCV 2014. Springer, Berlin, pp 391\u2013405"},{"issue":"2","key":"3229_CR35","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/0031-3203(81)90009-1","volume":"13","author":"DH Ballard","year":"1987","unstructured":"Ballard DH (1987) Generalizing the Hough transform to detect arbitrary shapes. Pattern Recogn 13(2):111\u2013122","journal-title":"Pattern Recogn"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-017-3229-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-017-3229-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-017-3229-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,22]],"date-time":"2019-09-22T03:39:47Z","timestamp":1569123587000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-017-3229-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,10,13]]},"references-count":35,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2019,7]]}},"alternative-id":["3229"],"URL":"https:\/\/doi.org\/10.1007\/s00521-017-3229-8","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2017,10,13]]},"assertion":[{"value":"2 May 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 October 2017","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 October 2017","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that there is no conflict of interest in this manuscript","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}