{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T18:52:18Z","timestamp":1770576738288,"version":"3.49.0"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2024,7,11]],"date-time":"2024-07-11T00:00:00Z","timestamp":1720656000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,11]],"date-time":"2024-07-11T00:00:00Z","timestamp":1720656000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFB1714800"],"award-info":[{"award-number":["2021YFB1714800"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFB1714800"],"award-info":[{"award-number":["2021YFB1714800"]}],"id":[{"id":"10.13039\/501100012166","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":[[2024,11]]},"DOI":"10.1007\/s13042-024-02213-4","type":"journal-article","created":{"date-parts":[[2024,7,11]],"date-time":"2024-07-11T12:12:24Z","timestamp":1720699944000},"page":"5091-5106","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Industrial product surface defect detection via the fast denoising diffusion implicit model"],"prefix":"10.1007","volume":"15","author":[{"given":"Yue","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingsheng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianghong","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haibin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhifeng","family":"Hao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ze","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Botao","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunyang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,11]]},"reference":[{"key":"2213_CR1","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.inffus.2018.10.005","volume":"50","author":"A Diez-Olivan","year":"2019","unstructured":"Diez-Olivan A, Del Ser J, Galar D, Sierra B (2019) Data fusion and machine learning for industrial prognosis: trends and perspectives towards industry 4.0. Inform Fus 50:92\u2013111","journal-title":"Inform Fus"},{"key":"2213_CR2","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.eng.2021.10.007","volume":"12","author":"J Chen","year":"2022","unstructured":"Chen J, Sun J, Wang G (2022) From unmanned systems to autonomous intelligent systems. Engineering 12:16\u201319","journal-title":"Engineering"},{"issue":"4","key":"2213_CR3","doi-asserted-by":"publisher","first-page":"1099","DOI":"10.1007\/s10845-021-01906-9","volume":"33","author":"T Schlosser","year":"2022","unstructured":"Schlosser T, Friedrich M, Beuth F, Kowerko D (2022) Improving automated visual fault inspection for semiconductor manufacturing using a hybrid multistage system of deep neural networks. J Intell Manuf 33(4):1099\u20131123","journal-title":"J Intell Manuf"},{"key":"2213_CR4","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1016\/j.jmsy.2021.10.006","volume":"61","author":"X Xu","year":"2021","unstructured":"Xu X, Lu Y, Vogel-Heuser B, Wang L (2021) Industry 4.0 and industry 5.0-inception, conception and perception. J Manuf Syst 61:530\u2013535","journal-title":"J Manuf Syst"},{"issue":"4","key":"2213_CR5","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/j.ndteint.2006.12.008","volume":"40","author":"JW Wilson","year":"2007","unstructured":"Wilson JW, Tian GY (2007) Pulsed electromagnetic methods for defect detection and characterisation. NDT & E Int 40(4):275\u2013283","journal-title":"NDT & E Int"},{"key":"2213_CR6","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1007\/s00170-018-2519-3","volume":"99","author":"RB Roy","year":"2018","unstructured":"Roy RB, Ghosh A, Bhattacharyya S, Mahto RP, Kumari K, Pal SK, Pal S (2018) Weld defect identification in friction stir welding through optimized wavelet transformation of signals and validation through x-ray micro-ct scan. Int J Adv Manuf Technol 99:623\u2013633","journal-title":"Int J Adv Manuf Technol"},{"issue":"10","key":"2213_CR7","doi-asserted-by":"publisher","first-page":"6743","DOI":"10.1109\/TII.2021.3126098","volume":"18","author":"B Yang","year":"2021","unstructured":"Yang B, Liu Z, Duan G, Tan J (2021) Mask2defect: a prior knowledge-based data augmentation method for metal surface defect inspection. IEEE Trans Ind Inf 18(10):6743\u20136755","journal-title":"IEEE Trans Ind Inf"},{"issue":"3","key":"2213_CR8","doi-asserted-by":"publisher","first-page":"642","DOI":"10.1109\/TII.2015.2417676","volume":"11","author":"M Win","year":"2015","unstructured":"Win M, Bushroa A, Hassan M, Hilman N, Ide-Ektessabi A (2015) A contrast adjustment thresholding method for surface defect detection based on mesoscopy. IEEE Trans Ind Inf 11(3):642\u2013649","journal-title":"IEEE Trans Ind Inf"},{"issue":"4","key":"2213_CR9","doi-asserted-by":"publisher","first-page":"3030","DOI":"10.1109\/TIE.2016.2643600","volume":"64","author":"H Gao","year":"2016","unstructured":"Gao H, Jin W, Yang X, Kaynak O (2016) A line-based-clustering approach for ball grid array component inspection in surface-mount technology. IEEE Trans Ind Electron 64(4):3030\u20133038","journal-title":"IEEE Trans Ind Electron"},{"issue":"6","key":"2213_CR10","doi-asserted-by":"publisher","first-page":"2783","DOI":"10.1007\/s10845-022-01962-9","volume":"34","author":"R Luo","year":"2023","unstructured":"Luo R, Chen R, Jia F, Lin B, Liu J, Sun Y, Yang X, Jia W (2023) Rbd-net: robust breakage detection algorithm for industrial leather. J Intell Manuf 34(6):2783\u20132796","journal-title":"J Intell Manuf"},{"issue":"7","key":"2213_CR11","doi-asserted-by":"publisher","first-page":"3091","DOI":"10.1007\/s10845-022-02000-4","volume":"34","author":"S Manivannan","year":"2023","unstructured":"Manivannan S (2023) Automatic quality inspection in additive manufacturing using semi-supervised deep learning. J Intell Manuf 34(7):3091\u20133108","journal-title":"J Intell Manuf"},{"issue":"2","key":"2213_CR12","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1109\/TSM.2021.3065405","volume":"34","author":"KC-C Cheng","year":"2021","unstructured":"Cheng KC-C, Chen LL-Y, Li J-W, Li KS-M, Tsai NC-Y, Wang S-J, Huang AY-A, Chou L, Lee C-S, Chen JE (2021) Machine learning-based detection method for wafer test induced defects. IEEE Trans Semicond Manuf 34(2):161\u2013167","journal-title":"IEEE Trans Semicond Manuf"},{"key":"2213_CR13","doi-asserted-by":"publisher","first-page":"823","DOI":"10.1007\/s10845-020-01591-0","volume":"32","author":"C-Y Hsu","year":"2021","unstructured":"Hsu C-Y, Liu W-C (2021) Multiple time-series convolutional neural network for fault detection and diagnosis and empirical study in semiconductor manufacturing. J Intell Manuf 32:823\u2013836","journal-title":"J Intell Manuf"},{"key":"2213_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.rcim.2019.101825","volume":"61","author":"Y Gao","year":"2020","unstructured":"Gao Y, Gao L, Li X, Yan X (2020) A semi-supervised convolutional neural network-based method for steel surface defect recognition. Robot Comput-Integr Manuf 61:101825","journal-title":"Robot Comput-Integr Manuf"},{"key":"2213_CR15","first-page":"1","volume":"70","author":"M Niu","year":"2021","unstructured":"Niu M, Wang Y, Song K, Wang Q, Zhao Y, Yan Y (2021) An adaptive pyramid graph and variation residual-based anomaly detection network for rail surface defects. IEEE Trans Instrum Meas 70:1\u201313","journal-title":"IEEE Trans Instrum Meas"},{"key":"2213_CR16","doi-asserted-by":"crossref","unstructured":"Peng H, Zhang J, Huang X, et al. Unsupervised Social Bot Detection via Structural Information Theory. arXiv preprint arXiv:2404.13595, 2024.","DOI":"10.1145\/3660522"},{"issue":"10","key":"2213_CR17","doi-asserted-by":"publisher","first-page":"2553","DOI":"10.1007\/s11263-023-01822-w","volume":"131","author":"J Diers","year":"2023","unstructured":"Diers J, Pigorsch C (2023) A survey of methods for automated quality control based on images. Int J Comput Vis 131(10):2553\u20132581","journal-title":"Int J Comput Vis"},{"key":"2213_CR18","doi-asserted-by":"publisher","first-page":"43370","DOI":"10.1109\/ACCESS.2023.3271748","volume":"11","author":"M Prunella","year":"2023","unstructured":"Prunella M, Scardigno RM, Buongiorno D, Brunetti A, Longo N, Carli R, Dotoli M, Bevilacqua V (2023) Deep learning for automatic vision-based recognition of industrial surface defects: a survey. IEEE Access 11:43370\u201343423","journal-title":"IEEE Access"},{"key":"2213_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108396","volume":"123","author":"S Niu","year":"2022","unstructured":"Niu S, Li B, Wang X, He S, Peng Y (2022) Defect attention template generation cyclegan for weakly supervised surface defect segmentation. Pattern Recogn 123:108396","journal-title":"Pattern Recogn"},{"issue":"7","key":"2213_CR20","doi-asserted-by":"publisher","first-page":"4531","DOI":"10.1109\/TII.2021.3127188","volume":"18","author":"S Niu","year":"2021","unstructured":"Niu S, Li B, Wang X, Peng Y (2021) Region-and strength-controllable Gan for defect generation and segmentation in industrial images. IEEE Trans Ind Inf 18(7):4531\u20134541","journal-title":"IEEE Trans Ind Inf"},{"issue":"4","key":"2213_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3378889","volume":"6","author":"S Wang","year":"2020","unstructured":"Wang S, Cao J, Chen H, Peng H, Huang Z (2020) Seqst-gan: Seq2seq generative adversarial nets for multi-step urban crowd flow prediction. ACM Trans Spat Algorithms Syst (TSAS) 6(4):1\u201324","journal-title":"ACM Trans Spat Algorithms Syst (TSAS)"},{"key":"2213_CR22","unstructured":"Sohl-Dickstein J, Weiss E, Maheswaranathan N, Ganguli S (2015) Deep unsupervised learning using nonequilibrium thermodynamics. In: International Conference on Machine Learning. PMLR, pp 2256\u20132265"},{"key":"2213_CR23","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho J, Jain A, Abbeel P (2020) Denoising diffusion probabilistic models. Adv Neural Inf Process Syst 33:6840\u20136851","journal-title":"Adv Neural Inf Process Syst"},{"key":"2213_CR24","doi-asserted-by":"crossref","unstructured":"Wyatt J, Leach A, Schmon SM, Willcocks CG (2022) Anoddpm: anomaly detection with denoising diffusion probabilistic models using simplex noise. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 650\u2013656","DOI":"10.1109\/CVPRW56347.2022.00080"},{"key":"2213_CR25","doi-asserted-by":"crossref","unstructured":"Lugmayr A, Danelljan M, Romero A, Yu F, Timofte R, Van\u00a0Gool L (2022) Repaint: Inpainting using denoising diffusion probabilistic models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 11461\u201311471","DOI":"10.1109\/CVPR52688.2022.01117"},{"key":"2213_CR26","unstructured":"Zhang H, Wang Z, Wu Z, Jiang Y-G (2023) Diffusionad: denoising diffusion for anomaly detection. arXiv preprint arXiv:2303.08730"},{"key":"2213_CR27","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2023.3267522","author":"P Chen","year":"2023","unstructured":"Chen P, Xu C, Ma Z, Jin Y (2023) A mixed samples-driven methodology based on denoising diffusion probabilistic model for identifying damage in carbon fiber composite structures. IEEE Trans Instrum Meas. https:\/\/doi.org\/10.1109\/TIM.2023.3267522","journal-title":"IEEE Trans Instrum Meas"},{"key":"2213_CR28","unstructured":"Song J, Meng C, Ermon S (2020) Denoising diffusion implicit models. In: International Conference on Learning Representations"},{"key":"2213_CR29","unstructured":"Zhang Q, Chen Y (2022) Fast sampling of diffusion models with exponential integrator. In: The International Conference on Learning Representations"},{"key":"2213_CR30","first-page":"5775","volume":"35","author":"C Lu","year":"2022","unstructured":"Lu C, Zhou Y, Bao F, Chen J, Li C, Zhu J (2022) Dpm-solver: a fast ode solver for diffusion probabilistic model sampling in around 10 steps. Adv Neural Inf Process Syst 35:5775\u20135787","journal-title":"Adv Neural Inf Process Syst"},{"key":"2213_CR31","unstructured":"Lu C, Zhou Y, Bao F, Chen J, Li C, Zhu J (2022) Dpm-solver++: Fast solver for guided sampling of diffusion probabilistic models. arXiv preprint arXiv:2211.01095"},{"key":"2213_CR32","unstructured":"Zheng H, Nie W, Vahdat A, Azizzadenesheli K, Anandkumar A (2023) Fast sampling of diffusion models via operator learning. In: International Conference on Machine Learning. PMLR, pp 42390\u201342402"},{"key":"2213_CR33","first-page":"21696","volume":"34","author":"D Kingma","year":"2021","unstructured":"Kingma D, Salimans T, Poole B, Ho J (2021) Variational diffusion models. Adv Neural Inf Process Syst 34:21696\u201321707","journal-title":"Adv Neural Inf Process Syst"},{"key":"2213_CR34","unstructured":"Song Y, Sohl-Dickstein J, Kingma DP, Kumar A, Ermon S, Poole B (2020) Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456"},{"key":"2213_CR35","doi-asserted-by":"crossref","unstructured":"Stewart GW, Sun J-g (1990) Matrix perturbation theory. (No Title)","DOI":"10.1137\/1032121"},{"key":"2213_CR36","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5\u20139, 2015, Proceedings, Part III 18. Springer, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2213_CR37","doi-asserted-by":"crossref","unstructured":"Liu Z, Mao H, Wu C-Y, Feichtenhofer C, Darrell T, Xie S (2022) A convnet for the 2020s. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976\u201311986","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"2213_CR38","first-page":"21997","volume":"34","author":"PA Papp","year":"2021","unstructured":"Papp PA, Martinkus K, Faber L, Wattenhofer R (2021) Dropgnn: random dropouts increase the expressiveness of graph neural networks. Adv Neural Inf Process Syst 34:21997\u201322009","journal-title":"Adv Neural Inf Process Syst"},{"key":"2213_CR39","unstructured":"Zhang B, Fan C, Liu S, Huang K, Zhao X, Huang J, Liu Z (2023) The expressive power of graph neural networks: a survey. arXiv preprint arXiv:2308.08235"},{"key":"2213_CR40","doi-asserted-by":"publisher","first-page":"2547","DOI":"10.1109\/OJCOMS.2021.3128637","volume":"2","author":"S He","year":"2021","unstructured":"He S, Xiong S, Ou Y, Zhang J, Wang J, Huang Y, Zhang Y (2021) An overview on the application of graph neural networks in wireless networks. IEEE Open J Commun Soc 2:2547\u20132565","journal-title":"IEEE Open J Commun Soc"},{"key":"2213_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107131","volume":"126","author":"Q Ma","year":"2023","unstructured":"Ma Q, Zhang E, Chen Y, Duan J, Shao L (2023) Sia-net: structural information awareness network based on normal samples for surface defect detection. Eng Appl Artif Intell 126:107131","journal-title":"Eng Appl Artif Intell"},{"key":"2213_CR42","doi-asserted-by":"crossref","unstructured":"Wang X, Li D, Bu W (2022) Patch density estimation for anomaly detection with deep pyramid features. In: 2022 China Automation Congress (CAC). IEEE, pp. 3383\u20133388","DOI":"10.1109\/CAC57257.2022.10056091"},{"key":"2213_CR43","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1007\/s00371-018-1588-5","volume":"36","author":"Y Huang","year":"2020","unstructured":"Huang Y, Qiu C, Yuan K (2020) Surface defect saliency of magnetic tile. Vis Comput 36:85\u201396","journal-title":"Vis Comput"},{"issue":"13","key":"2213_CR44","first-page":"303","volume":"2020","author":"W Huang","year":"2020","unstructured":"Huang W, Wei P, Zhang M, Liu H (2020) Hripcb: a challenging dataset for pcb defects detection and classification. J Eng 2020(13):303\u2013309","journal-title":"J Eng"},{"key":"2213_CR45","doi-asserted-by":"crossref","unstructured":"Howard A, Sandler M, Chu G, Chen L-C, Chen B, Tan M, Wang W, Zhu Y, Pang R, Vasudevan V (2019) Searching for mobilenetv3. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1314\u20131324","DOI":"10.1109\/ICCV.2019.00140"},{"key":"2213_CR46","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S (2020) An image is worth 16x16 words: transformers for image recognition at scale. In: International Conference on Learning Representations"},{"key":"2213_CR47","doi-asserted-by":"crossref","unstructured":"Ding X, Zhang X, Ma N, Han J, Ding G, Sun J (2021) Repvgg: making vgg-style convnets great again. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13733\u201313742","DOI":"10.1109\/CVPR46437.2021.01352"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02213-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02213-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02213-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T05:27:45Z","timestamp":1728451665000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02213-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,11]]},"references-count":47,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["2213"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02213-4","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,11]]},"assertion":[{"value":"26 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 May 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 July 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}