{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T04:40:20Z","timestamp":1738384820399,"version":"3.35.0"},"reference-count":30,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. &amp; Syst."],"published-print":{"date-parts":[[2025,2,1]]},"DOI":"10.1587\/transinf.2024edp7049","type":"journal-article","created":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T22:11:45Z","timestamp":1726092705000},"page":"147-156","source":"Crossref","is-referenced-by-count":0,"title":["Multi-Scale Rail Surface Anomaly Detection Based on Weighted Multivariate Gaussian Distribution"],"prefix":"10.1587","volume":"E108.D","author":[{"given":"Yuyao","family":"LIU","sequence":"first","affiliation":[{"name":"Key Laboratory of Big Data &amp; Artificial Intelligence in Transportation (Beijing Jiaotong University), Ministry of Education"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingyong","family":"LI","sequence":"additional","affiliation":[{"name":"Key Laboratory of Big Data &amp; Artificial Intelligence in Transportation (Beijing Jiaotong University), Ministry of Education"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shi","family":"BAO","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Inner Mongolia University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wen","family":"WANG","sequence":"additional","affiliation":[{"name":"Key Laboratory of Big Data &amp; Artificial Intelligence in Transportation (Beijing Jiaotong University), Ministry of Education"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"publisher","unstructured":"[1] J. Gan, Q. Li, J. Wang, and H. Yu, \u201cA hierarchical extractor-based visual rail surface inspection system,\u201d IEEE Sensors J., vol.17, no.23, pp.7935-7944, 2017. 10.1109\/jsen.2017.2761858","DOI":"10.1109\/JSEN.2017.2761858"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] H. Yu, Q. Li, Y. Tan, J. Gan, J. Wang, Y.-a. Geng, and L. Jia, \u201cA coarse-to-fine model for rail surface defect detection,\u201d IEEE Trans. Instrum. Meas., vol.68, no.3, pp.656-666, 2019. 10.1109\/tim.2018.2853958","DOI":"10.1109\/TIM.2018.2853958"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] Q. Luo, X. Fang, J. Su, J. Zhou, B. Zhou, C. Yang, L. Liu, W. Gui, and L. Tian, \u201cAutomated visual defect classification for flat steel surface: A survey,\u201d IEEE Trans. Instrum. Meas., vol.69, no.12, pp.9329-9349, 2020. 10.1109\/tim.2020.3030167","DOI":"10.1109\/TIM.2020.3030167"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] W. Cui, J. Wang, H. Yu, W.F. Peng, L. Wang, S. Wang, P. Dai, and Q. Li, \u201cFrom digital model to reality application: A domain adaptation method for rail defect detection,\u201d Chinese Conference on Pattern Recognition and Computer Vision, 2021. 10.1007\/978-3-030-88007-1_10","DOI":"10.1007\/978-3-030-88007-1_10"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] X. Ni, Z. Ma, J. Liu, B. Shi, and H. Liu, \u201cAttention network for rail surface defect detection via consistency of intersection-over-union(iou)-guided center-point estimation,\u201d IEEE Trans. Ind. Informat., vol.18, no.3, pp.1694-1705, 2022. 10.1109\/tii.2021.3085848","DOI":"10.1109\/TII.2021.3085848"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] Y. Wu, Y. Qin, Y. Qian, F. Guo, Z. Wang, and L. Jia, \u201cHybrid deep learning architecture for rail surface segmentation and surface defect detection,\u201d Computer\u2010Aided Civil and Infrastructure Engineering, vol.37, no.2, pp.227-244, 2021. 10.1111\/mice.12710","DOI":"10.1111\/mice.12710"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] R. Saiku, J. Sato, T. Yamada, and K. Ito, \u201cEnhancing anomaly detection performance and acceleration,\u201d IEEJ Journal of Industry Applications, vol.11, no.4, pp.616-622, 2022. 10.1541\/ieejjia.21013871","DOI":"10.1541\/ieejjia.21013871"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] H. Tian, K. Guo, X. Guan, and Z. Wu, \u201cAnomaly detection of network traffic based on intuitionistic fuzzy set ensemble,\u201d IEICE Trans. Commun., vol.E106-B, no.7, pp.538-546, 2023. 10.1587\/transcom.2022ebp3147","DOI":"10.1587\/transcom.2022EBP3147"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] W. Shao, R. Kawakami, and T. Naemura, \u201cAnomaly detection using spatio-temporal context learned by video clip sorting,\u201d IEICE Trans. Inf. &amp; Syst., vol.E105-D, no.5, pp.1094-1102, 2022. 10.1587\/transinf.2021edp7207","DOI":"10.1587\/transinf.2021EDP7207"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] T. Defard, A. Setkov, A. Loesch, and R. Audigier, \u201cPadim: a patch distribution modeling framework for anomaly detection and localization,\u201d International Conference on Pattern Recognition, Springer, pp.475-489, 2021. 10.1007\/978-3-030-68799-1_35","DOI":"10.1007\/978-3-030-68799-1_35"},{"key":"11","unstructured":"[11] C.-L. Li, K. Sohn, J. Yoon, and T. Pfister, \u201cCutpaste: Self-supervised learning for anomaly detection and localization,\u201d Proc. IEEE\/CVF conference on computer vision and pattern recognition, pp.9664-9674, 2021. 10.1109\/cvpr46437.2021.00954"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] J.K. Jang, E. Hwang, and S.-H. Park, \u201cN-pad: Neighboring pixel-based industrial anomaly detection,\u201d Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp.4365-4374, 2023. 10.1109\/cvprw59228.2023.00459","DOI":"10.1109\/CVPRW59228.2023.00459"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] J. Bae, J.-H. Lee, and S. Kim, \u201cPni: Industrial anomaly detection using position and neighborhood information,\u201d 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France, pp.6350-6360, 2023. 10.1109\/iccv51070.2023.00586","DOI":"10.1109\/ICCV51070.2023.00586"},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] M. Niu, Y. Wang, K. Song, Q. Wang, Y. Zhao, and Y. Yan, \u201cAn adaptive pyramid graph and variation residual-based anomaly detection network for rail surface defects,\u201d IEEE Trans. Instrum. Meas., vol.70, pp.1-13, 2021. 10.1109\/tim.2021.3125987","DOI":"10.1109\/TIM.2021.3125987"},{"key":"15","unstructured":"[15] D.P. Kingma and M. Welling, \u201cAuto-encoding variational bayes,\u201d International Conference on Learning Representations, 2013."},{"key":"16","doi-asserted-by":"publisher","unstructured":"[16] V. Zavrtanik, M. Kristan, and D. Sko\u010daj, \u201cReconstruction by inpainting for visual anomaly detection,\u201d Pattern Recognition, vol.112, p.107706, 2021. 10.1016\/j.patcog.2020.107706","DOI":"10.1016\/j.patcog.2020.107706"},{"key":"17","doi-asserted-by":"publisher","unstructured":"[17] L. Wang, D. Zhang, J. Guo, and Y. Han, \u201cImage anomaly detection using normal data only by latent space resampling,\u201d Applied Sciences, vol.10, no.23, p.8660, 2020. 10.3390\/app10238660","DOI":"10.3390\/app10238660"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] V. Zavrtanik, M. Kristan, and D. Sko\u010daj, \u201cDraem-a discriminatively trained reconstruction embedding for surface anomaly detection,\u201d Proc. IEEE\/CVF International Conference on Computer Vision, pp.8310-8319, 2021. 10.1109\/iccv48922.2021.00822","DOI":"10.1109\/ICCV48922.2021.00822"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[19] K. Sato, S. Nakata, T. Matsubara, and K. Uehara, \u201cFew-shot anomaly detection using deep generative models for grouped data,\u201d IEICE Trans. Inf. &amp; Syst., vol.E105-D, no.2, pp.436-440, 2022. 10.1587\/transinf.2021edl8063","DOI":"10.1587\/transinf.2021EDL8063"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] D. Gong, L. Liu, V. Le, B. Saha, M.R. Mansour, S. Venkatesh, and A. Van Den Hengel, \u201cMemorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection,\u201d 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp.1705-1714, 2019. 10.1109\/iccv.2019.00179","DOI":"10.1109\/ICCV.2019.00179"},{"key":"21","doi-asserted-by":"publisher","unstructured":"[21] J. Liu, K. Song, M. Feng, Y. Yan, Z. Tu, and L. Zhu, \u201cSemi-supervised anomaly detection with dual prototypes autoencoder for industrial surface inspection,\u201d Optics and Lasers in Engineering, vol.136, p.106324, 2021. 10.1016\/j.optlaseng.2020.106324","DOI":"10.1016\/j.optlaseng.2020.106324"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] T. Schlegl, P. Seeb\u00f6ck, S.M. Waldstein, U. Schmidt-Erfurth, and G. Langs, \u201cUnsupervised anomaly detection with generative adversarial networks to guide marker discovery,\u201d International conference on information processing in medical imaging, Springer, pp.146-157, 2017. 10.1007\/978-3-319-59050-9_12","DOI":"10.1007\/978-3-319-59050-9_12"},{"key":"23","doi-asserted-by":"crossref","unstructured":"[23] S. Akcay, A. Atapour-Abarghouei, and T.P. Breckon, \u201cGanomaly: Semi-supervised anomaly detection via adversarial training,\u201d Computer Vision-ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, Dec. 2-6, 2018, Revised Selected Papers, Part III 14. Springer, pp.622-637, 2019. 10.1007\/978-3-030-20893-6_39","DOI":"10.1007\/978-3-030-20893-6_39"},{"key":"24","doi-asserted-by":"crossref","unstructured":"[24] P. Perera, R. Nallapati, and B. Xiang, \u201cOcgan: One-class novelty detection using gans with constrained latent representations,\u201d 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.2893-2901, 2019. 10.1109\/cvpr.2019.00301","DOI":"10.1109\/CVPR.2019.00301"},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] K. Roth, L. Pemula, J. Zepeda, B. Sch\u00f6lkopf, T. Brox, and P. Gehler, \u201cTowards total recall in industrial anomaly detection,\u201d Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp.14298-14308, 2022. 10.1109\/cvpr52688.2022.01392","DOI":"10.1109\/CVPR52688.2022.01392"},{"key":"26","doi-asserted-by":"publisher","unstructured":"[26] S. Lee, S. Lee, and B.C. Song, \u201cCfa: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization,\u201d IEEE Access, vol.10, pp.78446-78454, 2022. 10.1109\/access.2022.3193699","DOI":"10.1109\/ACCESS.2022.3193699"},{"key":"27","doi-asserted-by":"crossref","unstructured":"[27] J. Yi and S. Yoon, \u201cPatch svdd: Patch-level svdd for anomaly detection and segmentation,\u201d Proc. Asian conference on computer vision, pp.375-390, 2020. 10.1007\/978-3-030-69544-6_23","DOI":"10.1007\/978-3-030-69544-6_23"},{"key":"28","doi-asserted-by":"crossref","unstructured":"[28] Z. Liu, Y. Zhou, Y. Xu, and Z. Wang, \u201cSimplenet: A simple network for image anomaly detection and localization,\u201d Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp.20402-20411, 2023. 10.1109\/cvpr52729.2023.01954","DOI":"10.1109\/CVPR52729.2023.01954"},{"key":"29","doi-asserted-by":"publisher","unstructured":"[29] T. Yang, Y. Liu, Y. Huang, J. Liu, and S. Wang, \u201cSymmetry-driven unsupervised abnormal object detection for railway inspection,\u201d IEEE Trans. Ind. Informat., vol.19, no.12, pp.11487-11498, 2023. 10.1109\/tii.2023.3246995","DOI":"10.1109\/TII.2023.3246995"},{"key":"30","doi-asserted-by":"publisher","unstructured":"[30] S. Ma, K. Song, M. Niu, H. Tian, Y. Wang, and Y. Yan, \u201cShape-consistent one-shot unsupervised domain adaptation for rail surface defect segmentation,\u201d IEEE Trans. Ind. Informat., vol.19, no.9, pp.9667-9679, 2023. 10.1109\/tii.2022.3233654","DOI":"10.1109\/TII.2022.3233654"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E108.D\/2\/E108.D_2024EDP7049\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T03:31:06Z","timestamp":1738380666000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E108.D\/2\/E108.D_2024EDP7049\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,1]]},"references-count":30,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2024edp7049","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"type":"print","value":"0916-8532"},{"type":"electronic","value":"1745-1361"}],"subject":[],"published":{"date-parts":[[2025,2,1]]},"article-number":"2024EDP7049"}}