{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T05:12:41Z","timestamp":1780463561766,"version":"3.54.1"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2021,2,8]],"date-time":"2021-02-08T00:00:00Z","timestamp":1612742400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,2,8]],"date-time":"2021-02-08T00:00:00Z","timestamp":1612742400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52075027"],"award-info":[{"award-number":["52075027"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2021,6]]},"DOI":"10.1007\/s13042-021-01274-z","type":"journal-article","created":{"date-parts":[[2021,2,9]],"date-time":"2021-02-09T06:04:04Z","timestamp":1612850644000},"page":"1789-1801","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Fault detection of railway freight cars mechanical components based on multi-feature fusion convolutional neural network"],"prefix":"10.1007","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8202-0571","authenticated-orcid":false,"given":"Tao","family":"Ye","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhihao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongran","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuqiang","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,2,8]]},"reference":[{"key":"1274_CR1","first-page":"26","volume":"5","author":"R Liu","year":"2005","unstructured":"Liu R, Wang Y (2005) Principle and application of TFDS. Chin Railw 5:26\u201327","journal-title":"Chin Railw"},{"key":"1274_CR2","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D et al (2016) SSD: Single shot multibox detector. In. Lecture Notes in Computer Science European conference on computer vision (ECCV), Springer, Cham, pp 21\u201337","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"1274_CR3","unstructured":"Li Z X, Zhou F Q (2017) FSSD: Feature fusion single shot multibox detector. arXiv preprint arXiv:1712.00960"},{"key":"1274_CR4","doi-asserted-by":"crossref","unstructured":"Liu S, Huang D, Wang Y (2018) Receptive field block net for accurate and fast object detection. In: Lecture Notes in Computer Science Proceedings of the European Conference on computer vision (ECCV), pp 404\u2013419","DOI":"10.1007\/978-3-030-01252-6_24"},{"key":"1274_CR5","doi-asserted-by":"crossref","unstructured":"Liu Z, Xiao D, Chen Y (2012) Displacement fault detection of bearing weight saddle in TFDS based on though transform and symmetry validation. In: 9th International Conference on fuzzy systems and knowledge discovery 2012, IEEE, pp 1404\u20131408","DOI":"10.1109\/FSKD.2012.6234124"},{"key":"1274_CR6","doi-asserted-by":"crossref","unstructured":"Xia Y, Xie F, Jiang Z (2010) Broken railway fastener detection based on Adaboost algorithm. In: International Conference on optoelectronics and image processing 2010, IEEE, 1, pp 313\u2013316.","DOI":"10.1109\/ICOIP.2010.303"},{"issue":"1","key":"1274_CR7","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1109\/TIM.2015.2479101","volume":"65","author":"L Liu","year":"2015","unstructured":"Liu L, Zhou F, He Y (2015) Automated visual inspection system for bogie block key under complex freight train environment. IEEE Trans Instrum Meas 65(1):2\u201314","journal-title":"IEEE Trans Instrum Meas"},{"issue":"3","key":"1274_CR8","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1631\/FITEE.1400305","volume":"16","author":"R Zou","year":"2015","unstructured":"Zou R, Xu ZY, Li JY et al (2015) Real-time monitoring of brake shoe keys in freight cars. Front Inf Technol Electron Eng 16(3):191\u2013204","journal-title":"Front Inf Technol Electron Eng"},{"key":"1274_CR9","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1186\/s13640-017-0241-y","volume":"1","author":"Y Min","year":"2018","unstructured":"Min Y, Xiao B, Dang J et al (2018) Real time detection system for rail surface defects based on machine vision. EURASIP J Image Video Process 1:3","journal-title":"EURASIP J Image Video Process"},{"key":"1274_CR10","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.neucom.2016.10.018","volume":"222","author":"J Sun","year":"2017","unstructured":"Sun J, Xiao Z, Xie Y (2017) Automatic multi-fault recognition in TFDS based on convolutional neural network. Neurocomputing 222:127\u2013136","journal-title":"Neurocomputing"},{"issue":"2","key":"1274_CR11","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1109\/TIM.2017.2775345","volume":"67","author":"J Chen","year":"2017","unstructured":"Chen J, Liu Z, Wang H et al (2017) Automatic defect detection of fasteners on the catenary support device using deep convolutional neural network. IEEE Trans Instrum Meas 67(2):257\u2013269","journal-title":"IEEE Trans Instrum Meas"},{"key":"1274_CR12","first-page":"1","volume":"2018","author":"X Xu","year":"2018","unstructured":"Xu X, Lei Y, Yang F (2018) Railway subgrade defect automatic recognition method based on improved Faster R-CNN. Sci Program 2018:1\u201312","journal-title":"Sci Program"},{"issue":"3","key":"1274_CR13","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1177\/0954409718793464","volume":"233","author":"F Zhou","year":"2019","unstructured":"Zhou F, Li J, Li X et al (2019) Freight car target detection in a complex background based on convolutional neural networks. Proc Inst Mech Eng Part F J Rail Rapid Transit 233(3):298\u2013311","journal-title":"Proc Inst Mech Eng Part F J Rail Rapid Transit"},{"issue":"4","key":"1274_CR14","first-page":"320","volume":"47","author":"DJ Chen","year":"2017","unstructured":"Chen DJ, Zhang WS, Yang Y (2017) Detection and recognition of high-speed railway catenary locator based on deep learning. J Univ Sci Technol China 47(4):320\u2013327","journal-title":"J Univ Sci Technol China"},{"key":"1274_CR15","unstructured":"Krizhevsky A, Sutskever I, Hinton G E (2012) Imagenet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on neural information processing systems\u2014Volume 1 (NIPS'12),pp 1097\u20131105"},{"key":"1274_CR16","unstructured":"Sermanet P, Eigen D, Zhang X et al (2013) Overfeat: integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229."},{"key":"1274_CR17","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T et al (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp 580\u2013587.","DOI":"10.1109\/CVPR.2014.81"},{"key":"1274_CR18","doi-asserted-by":"crossref","unstructured":"Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE International Conference on computer vision, pp 1440\u20131448.","DOI":"10.1109\/ICCV.2015.169"},{"issue":"6","key":"1274_CR19","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2015","unstructured":"Ren S, He KM, Girshick R et al (2015) Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137\u20131149","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1274_CR20","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R et al (2016) You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on computer vision and pattern recognition, pp 779\u2013788.","DOI":"10.1109\/CVPR.2016.91"},{"key":"1274_CR21","doi-asserted-by":"crossref","unstructured":"Lin T Y, Doll\u00e1r P, Girshick R et al (2017) Feature pyramid networks for object detection. In Proceedings of the IEEE Conference on computer vision and pattern recognition, pp 2117\u20132125","DOI":"10.1109\/CVPR.2017.106"},{"key":"1274_CR22","unstructured":"Fu C Y, Liu W, Ranga A et al (2017) DSSD: Deconvolutional single shot detector. arXiv preprint arXiv:1701.06659."},{"key":"1274_CR23","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y (2015) Going deep with convolutions. In: Proceedings of the IEEE Conference on computer vision and pattern recognition (CVPR), Boston, MA, pp 1\u20139.","DOI":"10.1109\/CVPR.2015.7298594"},{"issue":"8","key":"1274_CR24","doi-asserted-by":"publisher","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","volume":"42","author":"J Hu","year":"2017","unstructured":"Hu J, Shen L, Sun G et al (2017) Squeeze-and-excitation networks. IEEE Trans Pattern Anal Mach Intell 42(8):2011\u20132023","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1274_CR25","doi-asserted-by":"crossref","unstructured":"Zhang S, Wen L, Bian X et al (2018) Single-shot refinement neural network for object detection. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pp 4203\u20134212","DOI":"10.1109\/CVPR.2018.00442"},{"key":"1274_CR26","unstructured":"Howard A G, Zhu M, Chen B et al (2017) MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861"},{"key":"1274_CR27","doi-asserted-by":"crossref","unstructured":"Zhang X, Zhou X, Lin M et al (2017) ShuffleNet: An extremely efficient convolutional neural network for mobile devices. In Proceedings of the IEEE Conference on computer vision and pattern recognition, pp 6848\u20136856","DOI":"10.1109\/CVPR.2018.00716"},{"key":"1274_CR28","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"1274_CR29","doi-asserted-by":"crossref","unstructured":"He K M, Zhang X Y, Ren S Q et al (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, NV,\u00a0pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"2","key":"1274_CR30","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","volume":"42","author":"TY Lin","year":"2018","unstructured":"Lin TY, Goyal P, Girshick R et al (2018) Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell 42(2):318\u2013327","journal-title":"IEEE Trans Pattern Anal Mach Intell"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-021-01274-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-021-01274-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-021-01274-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,20]],"date-time":"2021-05-20T09:17:18Z","timestamp":1621502238000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-021-01274-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,8]]},"references-count":30,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2021,6]]}},"alternative-id":["1274"],"URL":"https:\/\/doi.org\/10.1007\/s13042-021-01274-z","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,8]]},"assertion":[{"value":"18 December 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 January 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 February 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}