{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T09:43:23Z","timestamp":1768643003641,"version":"3.49.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"41","license":[{"start":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T00:00:00Z","timestamp":1711497600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T00:00:00Z","timestamp":1711497600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"publisher","award":["2022JJ60075"],"award-info":[{"award-number":["2022JJ60075"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-19011-3","type":"journal-article","created":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T03:01:55Z","timestamp":1711508515000},"page":"88919-88947","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["SCA-GANomaly: an unsupervised anomaly detection model of high-speed railway catenary components"],"prefix":"10.1007","volume":"83","author":[{"given":"Shijie","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2580-1560","authenticated-orcid":false,"given":"Qijie","family":"Zou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bing","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,3,27]]},"reference":[{"issue":"2","key":"19011_CR1","first-page":"31","volume":"36","author":"Y Han","year":"2014","unstructured":"Han Y, Liu Z, Han Z, Yang HM (2014) Fracture detection of ear pieces of catenary support devices of high-speed railway based on SIFT feature matching. J China Railway Soc 36(2):31\u201336","journal-title":"J China Railway Soc"},{"issue":"5","key":"19011_CR2","first-page":"40","volume":"39","author":"G Zhang","year":"2017","unstructured":"Zhang G, Liu Z, Han Y, Han Z (2017) Loss fault detection for auxiliary catenary wire of high-speed railway catenary wire holder. J China Railway Soc 39(5):40\u201346","journal-title":"J China Railway Soc"},{"key":"19011_CR3","unstructured":"Yang H (2017) Detection of catenary insulator cracks and positioning supports based on image processing (Master's thesis, Southwest Jiaotong University). https:\/\/kns.cnki.net\/kcms2\/article\/abstract?v=La2KlAOQ31R0Uxrc-d2RShCIJlYb-0EzHq6pjtNtxNSJsEDaayU0zONwYGur9Bl_TgZ98_jTknT66jjh_-P1VwIjHvGVuOjHVmU9ejg5BEDYN2zf7CbrmpkrvfSxn9ALCdiUtXi4PZ4=&uniplatform=NZKPT&language=CHS. Accessed 28 Aug 2023"},{"key":"19011_CR4","doi-asserted-by":"publisher","unstructured":"Tan, P, Li, XF, Xu, JM, Ma, JE, Wang, FJ, Ding, J, ... Ning, Y (2020) Catenary insulator defect detection based on contour features and gray similarity matching. Journal of Zhejiang University-SCIENCE A, 21(1), 64\u201373 https:\/\/doi.org\/10.1631\/jzus.A1900341","DOI":"10.1631\/jzus.A1900341"},{"issue":"2","key":"19011_CR5","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1109\/TIM.2017.2775345","volume":"67","author":"J Chen","year":"2018","unstructured":"Chen J, Liu Z, Wang H, Nunez A, Han Z (2018) Automatic Defect Detection of Fasteners on the Catenary Support Device Using Deep Convolutional Neural Network. IEEE Trans Instrument Measurement 67(2):257\u2013269","journal-title":"IEEE Trans Instrument Measurement"},{"issue":"8","key":"19011_CR6","doi-asserted-by":"publisher","first-page":"2849","DOI":"10.1109\/TIM.2018.2871353","volume":"68","author":"J Zhong","year":"2018","unstructured":"Zhong J, Liu Z, Han Z, Han Y, Zhang W (2018) A CNN-based defect inspection method for catenary split pins in high-speed railway. IEEE Trans Instrum Meas 68(8):2849\u20132860","journal-title":"IEEE Trans Instrum Meas"},{"key":"19011_CR7","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1007\/978-981-15-2914-6_64","volume-title":"In Proceedings of the 4th International Conference on Electrical and Information Technologies for Rail Transportation (EITRT) 2019: Rail Transportation Information Processing and Operational Management Technologies","author":"J Cui","year":"2020","unstructured":"Cui J, Wu Y, Qin Y, Hou R (2020) Defect detection for catenary sling based on image processing and deep learning method. In: Proceedings of the 4th International Conference on Electrical and Information Technologies for Rail Transportation (EITRT) 2019: Rail Transportation Information Processing and Operational Management Technologies. Springer, Singapore, pp 675\u2013683. https:\/\/doi.org\/10.1007\/978-981-15-2914-6_64"},{"key":"19011_CR8","doi-asserted-by":"publisher","first-page":"556","DOI":"10.1016\/j.neucom.2018.10.107","volume":"396","author":"Y Han","year":"2020","unstructured":"Han Y, Liu Z, Lyu Y, Liu K, Li C, Zhang W (2020) Deep learning-based visual ensemble method for high-speed railway catenary clevis fracture detection. Neurocomputing 396:556\u2013568","journal-title":"Neurocomputing"},{"key":"19011_CR9","doi-asserted-by":"publisher","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) SSD: Single shot multibox detector. In: Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part I 14. Springer International Publishing, pp 21\u201337. https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"19011_CR10","doi-asserted-by":"publisher","unstructured":"Redmon J, Farhadi A (2018) Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767.  https:\/\/doi.org\/10.48550\/arXiv.1804.02767","DOI":"10.48550\/arXiv.1804.02767"},{"key":"19011_CR11","doi-asserted-by":"publisher","unstructured":"Bochkovskiy A, Wang CY, Liao HYM (2020) Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934.  https:\/\/doi.org\/10.48550\/arXiv.2004.10934","DOI":"10.48550\/arXiv.2004.10934"},{"key":"19011_CR12","doi-asserted-by":"publisher","unstructured":"Ge Z, Liu S, Wang F, Li Z, Sun J (2021) Yolox: Exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430.  https:\/\/doi.org\/10.48550\/arXiv.2107.08430","DOI":"10.48550\/arXiv.2107.08430"},{"key":"19011_CR13","doi-asserted-by":"publisher","unstructured":"Li C, Li L, Jiang H, Weng K, Geng Y, Li L, ..., Wei X (2022) YOLOv6: A single-stage object detection framework for industrial applications. arXiv preprint arXiv:2209.02976. https:\/\/doi.org\/10.48550\/arXiv.2209.02976","DOI":"10.48550\/arXiv.2209.02976"},{"key":"19011_CR14","doi-asserted-by":"publisher","first-page":"7464","DOI":"10.1109\/CVPR52729.2023.00721","volume-title":"In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"CY Wang","year":"2023","unstructured":"Wang CY, Bochkovskiy A, Liao HYM (2023) YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 7464\u20137475. https:\/\/doi.org\/10.1109\/CVPR52729.2023.00721"},{"key":"19011_CR15","doi-asserted-by":"publisher","unstructured":"Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp 1440\u20131448. https:\/\/doi.org\/10.1109\/ICCV.2015.169","DOI":"10.1109\/ICCV.2015.169"},{"issue":"06","key":"19011_CR16","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Trans Pattern Anal Mach Intell 39(06):1137\u20131149","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"19011_CR17","doi-asserted-by":"publisher","unstructured":"Breunig MM, Kriegel HP, Ng RT, Sander J (2000) LOF: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data 29(2):93\u2013104. https:\/\/doi.org\/10.1145\/335191.335388","DOI":"10.1145\/335191.335388"},{"key":"19011_CR18","doi-asserted-by":"publisher","unstructured":"Liu FT, Ting KM, Zhou ZH (2008) Isolation forest. In: 2008 Eighth IEEE International Conference on Data Mining. IEEE, pp 413\u2013422. https:\/\/doi.org\/10.1109\/ICDM.2008.17","DOI":"10.1109\/ICDM.2008.17"},{"issue":"3","key":"19011_CR19","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/0098-3004(93)90090-R","volume":"19","author":"A Ma\u0107kiewicz","year":"1993","unstructured":"Ma\u0107kiewicz A, Ratajczak W (1993) Principal components analysis (PCA). Comput Geosci 19(3):303\u2013342","journal-title":"Comput Geosci"},{"key":"19011_CR20","doi-asserted-by":"publisher","unstructured":"Pinaya WHL, Vieira S, Garcia-Dias R, Mechelli A (2020) Autoencoders. In: Machine learning. Academic Press, pp 193\u2013208. https:\/\/doi.org\/10.1016\/B978-0-12-815739-8.00011-0","DOI":"10.1016\/B978-0-12-815739-8.00011-0"},{"key":"19011_CR21","doi-asserted-by":"publisher","unstructured":"Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531.  https:\/\/doi.org\/10.48550\/arXiv.1503.02531","DOI":"10.48550\/arXiv.1503.02531"},{"key":"19011_CR22","doi-asserted-by":"publisher","unstructured":"Xing P, Li Z (2022) Asymmetric distillation post-segmentation method for image anomaly detection. arXiv preprint arXiv:2210.10495.  https:\/\/doi.org\/10.48550\/arXiv.2210.10495","DOI":"10.48550\/arXiv.2210.10495"},{"key":"19011_CR23","doi-asserted-by":"publisher","unstructured":"Reynolds, DA (2009) Gaussian mixture models. Encyclopedia of biometrics, 741(659\u2013663) https:\/\/doi.org\/10.1007\/978-0-387-73003-5_196","DOI":"10.1007\/978-0-387-73003-5_196"},{"key":"19011_CR24","doi-asserted-by":"publisher","unstructured":"Kingma DP, Welling M (2013) Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114.  https:\/\/doi.org\/10.48550\/arXiv.1312.6114","DOI":"10.48550\/arXiv.1312.6114"},{"key":"19011_CR25","unstructured":"Goodfellow, I, Pouget-Abadie, J, Mirza, M, Xu, B, Warde-Farley, D, Ozair, S, ... , Bengio, Y (2014) Generative adversarial nets. Advances in neural information processing systems, 27"},{"key":"19011_CR26","doi-asserted-by":"publisher","unstructured":"Schlegl, T, Seeb\u00f6ck, P, Waldstein, SM, Schmidt-Erfurth, U, Langs, G (2017) Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In International conference on information processing in medical imaging (pp. 146\u2013157). Cham: Springer International Publishing https:\/\/doi.org\/10.1007\/978-3-319-59050-9_12","DOI":"10.1007\/978-3-319-59050-9_12"},{"key":"19011_CR27","doi-asserted-by":"publisher","unstructured":"Zenati H, Foo CS, Lecouat B, Manek G, Chandrasekhar VR (2018) Efficient gan-based anomaly detection. arXiv preprint arXiv:1802.06222.  https:\/\/doi.org\/10.48550\/arXiv.1802.06222","DOI":"10.48550\/arXiv.1802.06222"},{"key":"19011_CR28","doi-asserted-by":"publisher","unstructured":"Akcay S, Atapour-Abarghouei A, Breckon TP (2019) Ganomaly: Semi-supervised anomaly detection via adversarial training. In: Computer Vision\u2013ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2\u20136, 2018, Revised Selected Papers, Part III 14. Springer International Publishing,\u00a0pp 622\u2013637. https:\/\/doi.org\/10.1007\/978-3-030-20893-6_39","DOI":"10.1007\/978-3-030-20893-6_39"},{"key":"19011_CR29","unstructured":"Chen Q (2021) Research on detection and defect identification algorithms of high-speed railway catenary components (Master's thesis, Zhejiang University). https:\/\/kns.cnki.net\/kcms2\/article\/abstract?v=La2KlAOQ31Q3C8T0dXTsYVSg89kqlLQ-WW76jRZ_4WndE0LCrfHReH3c2BjQ4Da_d3eCzOVv2idPLdDuB4qdY_PMHWycuLa-o6bXEK-Ra2lbESpWj52TPDmZk_OjhsjA9_f_63xGu0U=&uniplatform=NZKPT&language=CHS. Accessed 26 Aug 2023"},{"key":"19011_CR30","doi-asserted-by":"publisher","unstructured":"Ak\u00e7ay S, Atapour-Abarghouei A, Breckon TP (2019) Skip-ganomaly: Skip connected and adversarially trained encoder-decoder anomaly detection. In: 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, pp 1\u20138. https:\/\/doi.org\/10.1109\/IJCNN.2019.8851808","DOI":"10.1109\/IJCNN.2019.8851808"},{"key":"19011_CR31","unstructured":"Zhang B (2022) Research on status detection algorithm of key components of high-speed railway catenary based on deep learning (Master's thesis, Shijiazhuang Railway University). https:\/\/kns.cnki.net\/kcms2\/article\/abstract?v=La2KlAOQ31QR0QZZx4P0f-BWTVJ3_VzpjZmzCOJhaTc7PlMMG1XahqFB6c8Cx3f2cT-AkLrY2jT6SBrZwuQ2GPlTfzUqNHk6hLSPPdEz15t0-XNZvHa5uQycrgEUMWpNtIAFtAL5vHQ=&uniplatform=NZKPT&language=CHS. Accessed 26 Aug 2023"},{"key":"19011_CR32","doi-asserted-by":"publisher","unstructured":"Wang, Q, Zhou, X, Wang, C, Liu, Z, Huang, J, Zhou, Y, ... Cheng, JZ (2019) WGAN-based synthetic minority over-sampling technique: Improving semantic fine-grained classification for lung nodules in CT images. IEEE Access, 7, 18450\u201318463 https:\/\/doi.org\/10.1109\/ACCESS.2019.2896409","DOI":"10.1109\/ACCESS.2019.2896409"},{"issue":"8","key":"19011_CR33","doi-asserted-by":"publisher","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","volume":"42","author":"J Hu","year":"2020","unstructured":"Hu J, Shen L, Albanie S, Sun G, Wu E (2020) Squeeze-and-Excitation Networks. IEEE Trans Pattern Anal Mach Intell 42(8):2011\u20132023","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"19011_CR34","doi-asserted-by":"publisher","unstructured":"Woo, S, Park, J, Lee, JY, Kweon, IS (2018) Cbam: Convolutional block attention module. In Proceedings of the European conference on computer vision (ECCV) (pp. 3\u201319) https:\/\/doi.org\/10.1007\/978-3-030-01234-2_1","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"19011_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108792","volume":"130","author":"H Tang","year":"2022","unstructured":"Tang H, Yuan C, Li Z, Tang J (2022) Learning attention-guided pyramidal features for few-shot fine-grained recognition. Pattern Recogn 130:108792","journal-title":"Pattern Recogn"},{"key":"19011_CR36","doi-asserted-by":"publisher","unstructured":"Liu Y, Shao Z, Hoffmann N (2021) Global attention mechanism: retain information to enhance channel-spatial interactions. arXiv preprint arXiv:2112.05561.  https:\/\/doi.org\/10.48550\/arXiv.2112.05561","DOI":"10.48550\/arXiv.2112.05561"},{"key":"19011_CR37","unstructured":"Vaswani, A, Shazeer, N, Parmar, N, Uszkoreit, J, Jones, L, Gomez, AN, ... Polosukhin, I (2017) Attention is all you need. Advances in neural information processing systems, 30"},{"key":"19011_CR38","doi-asserted-by":"publisher","unstructured":"Roth K, Pemula L, Zepeda J, Sch\u00f6lkopf B, Brox T, Gehler P (2022) Towards total recall in industrial anomaly detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 14318\u201314328. https:\/\/doi.org\/10.1109\/cvpr52688.2022.01392","DOI":"10.1109\/cvpr52688.2022.01392"},{"key":"19011_CR39","doi-asserted-by":"publisher","unstructured":"Rudolph M, Wehrbein T, Rosenhahn B, Wandt B (2022) Fully convolutional cross-scale-flows for image-based defect detection. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp 1088\u20131097. https:\/\/doi.org\/10.1109\/WACV51458.2022.00189","DOI":"10.1109\/WACV51458.2022.00189"},{"key":"19011_CR40","doi-asserted-by":"publisher","unstructured":"Zavrtanik V, Kristan M, Sko\u010daj D (2021) Draem-a discriminatively trained reconstruction embedding for surface anomaly detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 8330\u20138339. https:\/\/doi.org\/10.1109\/iccv48922.2021.00822","DOI":"10.1109\/iccv48922.2021.00822"},{"key":"19011_CR41","doi-asserted-by":"publisher","unstructured":"Defard, T, Setkov, A, Loesch, A, Audigier, R (2021) Padim: a patch distribution modeling framework for anomaly detection and localization. In International Conference on Pattern Recognition (pp. 475\u2013489). Cham: Springer International Publishing. https:\/\/doi.org\/10.1007\/978-3-030-68799-1_35","DOI":"10.1007\/978-3-030-68799-1_35"},{"key":"19011_CR42","doi-asserted-by":"publisher","unstructured":"Deng H, Li X (2022) Anomaly detection via reverse distillation from one-class embedding. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 9737\u20139746. https:\/\/doi.org\/10.1109\/CVPR52688.2022.00951","DOI":"10.1109\/CVPR52688.2022.00951"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19011-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-19011-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19011-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T12:13:33Z","timestamp":1734956013000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-19011-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,27]]},"references-count":42,"journal-issue":{"issue":"41","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["19011"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-19011-3","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,27]]},"assertion":[{"value":"6 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 March 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 March 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 March 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}