{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T13:05:52Z","timestamp":1767704752654,"version":"3.37.3"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"13","license":[{"start":{"date-parts":[[2022,2,26]],"date-time":"2022-02-26T00:00:00Z","timestamp":1645833600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,2,26]],"date-time":"2022-02-26T00:00:00Z","timestamp":1645833600000},"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":["62002053"],"award-info":[{"award-number":["62002053"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2021A1515011866"],"award-info":[{"award-number":["2021A1515011866"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangdong Basic and Applied Basic Research Projects","award":["2019A1515111082"],"award-info":[{"award-number":["2019A1515111082"]}]},{"name":"Social Welfare Major Project of Zhongshan","award":["2019B2010","2019B2011"],"award-info":[{"award-number":["2019B2010","2019B2011"]}]},{"name":"Social Welfare Major Project of Zhongshan","award":["420S36"],"award-info":[{"award-number":["420S36"]}]},{"name":"Achievement Cultivation Project of Zhongshan Industrial Technology Research Institute","award":["419N26"],"award-info":[{"award-number":["419N26"]}]},{"DOI":"10.13039\/501100009330","name":"Science and Technology Foundation of Guangdong Province","doi-asserted-by":"crossref","award":["2021A010-1180005"],"award-info":[{"award-number":["2021A010-1180005"]}],"id":[{"id":"10.13039\/501100009330","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Young Innovative Talents Project of Education Department of Guangdong Province","award":["2018KQNCX337","2019KQNCX186"],"award-info":[{"award-number":["2018KQNCX337","2019KQNCX186"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1007\/s00521-022-07005-x","type":"journal-article","created":{"date-parts":[[2022,2,26]],"date-time":"2022-02-26T15:03:36Z","timestamp":1645887816000},"page":"10691-10705","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Defect detection of photovoltaic glass based on level set map"],"prefix":"10.1007","volume":"34","author":[{"given":"Shuai","family":"Dong","sequence":"first","affiliation":[]},{"given":"Chen","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Yihui","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Kun","family":"Zou","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2360-0466","authenticated-orcid":false,"given":"Guisong","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,2,26]]},"reference":[{"key":"7005_CR1","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1049\/ip-vis:20045131","volume":"152","author":"X Yang","year":"2005","unstructured":"Yang X, Pang G, Yung N (2005) Robust fabric defect detection and classification using multiple adaptive wavelets. Vis Image Signal Process 152:715\u2013723","journal-title":"Vis Image Signal Process"},{"key":"7005_CR2","doi-asserted-by":"publisher","first-page":"148413","DOI":"10.1109\/ACCESS.2019.2946062","volume":"7","author":"S Lin","year":"2019","unstructured":"Lin S, He Z, Sun L (2019) Defect enhancement generative adversarial network for enlarging data set of microcrack defect. IEEE Access 7:148413\u2013148423","journal-title":"IEEE Access"},{"key":"7005_CR3","doi-asserted-by":"crossref","unstructured":"Liu K, Li A, Wen X, Chen H, Yang P (2019) Steel surface defect detection using GAN and one-class classifier. In: International conference on automation and computing, pp 5\u20137. Chinese Automation and Computing Society in the UK - CACSUK","DOI":"10.23919\/IConAC.2019.8895110"},{"issue":"2","key":"7005_CR4","doi-asserted-by":"publisher","first-page":"1343","DOI":"10.1109\/TII.2019.2945403","volume":"16","author":"J Lian","year":"2020","unstructured":"Lian J, Jia W, Zareapoor M, Zheng Y, Luo R (2020) Deep-learning-based small surface defect detection via an exaggerated local variation-based generative adversarial network. IEEE Trans Ind Inf 16(2):1343\u20131351","journal-title":"IEEE Trans Ind Inf"},{"key":"7005_CR5","doi-asserted-by":"publisher","first-page":"108335","DOI":"10.1109\/ACCESS.2020.3001349","volume":"8","author":"H Bing","year":"2020","unstructured":"Bing H, Wang J (2020) Detection of PCB surface defects with improved faster-RCNN and feature pyramid network. IEEE Access 8:108335\u2013108345","journal-title":"IEEE Access"},{"issue":"1","key":"7005_CR6","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1109\/TII.2019.2937563","volume":"16","author":"Y Feng","year":"2020","unstructured":"Feng Y, Chen Z, Wang D, Chen J, Feng Z (2020) DeepWelding: a deep learning enhanced approach to GTAW using multisource sensing images. IEEE Trans Ind Inf 16(1):465\u2013474","journal-title":"IEEE Trans Ind Inf"},{"issue":"1","key":"7005_CR7","first-page":"1","volume":"158","author":"W Ming","year":"2020","unstructured":"Ming W, Shen F, Li X, Zhang Z, Jinguang D, Chen Z, Cao Y (2020) A comprehensive review of defect detection in 3C glass components. Measurement 158(1):1\u201320","journal-title":"Measurement"},{"issue":"13","key":"7005_CR8","first-page":"216","volume":"41","author":"C Zhang","year":"2020","unstructured":"Zhang C, Chen X, Yunjie X, Wei Z, Zhou S (2020) Surface defect detection system of glass. Packag Eng 41(13):216\u2013222","journal-title":"Packag Eng"},{"issue":"3","key":"7005_CR9","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1016\/j.asoc.2016.10.030","volume":"52","author":"C Jian","year":"2017","unstructured":"Jian C, Gao J, Ao Y (2017) Automatic surface defect detection for mobile phone screen glass based on machine vision. Appl Soft Comput J 52(3):348\u2013358","journal-title":"Appl Soft Comput J"},{"issue":"1","key":"7005_CR10","first-page":"134","volume":"39","author":"C Wang","year":"2020","unstructured":"Wang C, Huang Y, Zhang X, Shenglin L (2020) A Method of scratch defects detection for curved glass based on machine vision. Tech Autom Appl 39(1):134\u2013139","journal-title":"Tech Autom Appl"},{"issue":"4","key":"7005_CR11","doi-asserted-by":"publisher","first-page":"2189","DOI":"10.1109\/TII.2019.2935153","volume":"16","author":"X Zhou","year":"2020","unstructured":"Zhou X, Wang Y, Zhu Q, Mao J, Xiao C, Xiao L, Zhang H (2020) A surface defect detection framework for glass bottle bottom using visual attention model and wavelet transform. IEEE Trans Ind Inf 16(4):2189\u20132201","journal-title":"IEEE Trans Ind Inf"},{"key":"7005_CR12","doi-asserted-by":"publisher","first-page":"362","DOI":"10.4028\/www.scientific.net\/AMM.437.362","volume":"437","author":"HB Yao","year":"2013","unstructured":"Yao HB, Ping J, Ma GD, Li LW, Gu JN (2013) The system research on automatic defect detection of glasses. Appl Mech Mater 437:362\u2013365","journal-title":"Appl Mech Mater"},{"issue":"203\u2013212","key":"7005_CR13","first-page":"1","volume":"12","author":"NS Rosli","year":"2018","unstructured":"Rosli NS, Fauadi MHFM, Awang NF, Noor AZM (2018) Vision-based defects detection for glass production based on improved image processing method. J Adv Manuf Technol 12(203\u2013212):1\u2013224","journal-title":"J Adv Manuf Technol"},{"issue":"4","key":"7005_CR14","first-page":"900","volume":"26","author":"H Xiong","year":"2020","unstructured":"Xiong H, Fan C, Zhao S, Ying Yu (2020) Glass surface defect detection method based on multiscale convolution neural network. Comput Integr Manuf Syst 26(4):900\u2013909","journal-title":"Comput Integr Manuf Syst"},{"key":"7005_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107571","volume":"109","author":"J Zhang","year":"2020","unstructured":"Zhang J, Su H, Zou W, Gong X, Zhang Z, Shen F (2020) CADN: a weakly supervised learning-based category-aware object detection network for surface defect detection. Pattern Recognit 109:107571","journal-title":"Pattern Recognit"},{"key":"7005_CR16","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE international conference on computer vision, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"7005_CR17","unstructured":"Tan M, Le QV (2019) EfficientNet: Rethinking model scaling for convolutional neural networks. arXiv:1905.11946, pp 1\u201310"},{"key":"7005_CR18","unstructured":"Poudel RPK, Liwicki S, Cipolla R (2019) Fast-SCNN: fast semantic segmentation network. arXiv:1902.04502, pp 1\u201317"},{"key":"7005_CR19","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Dollar P, Girshick R (2017) Mask R-CNN. In: Proceedings of the IEEE international conference on computer vision, pp 2961\u20132969","DOI":"10.1109\/ICCV.2017.322"},{"issue":"6","key":"7005_CR20","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2015","unstructured":"Ren S, He K, Girshick R, Jian S (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"},{"issue":"15","key":"7005_CR21","doi-asserted-by":"publisher","first-page":"11597","DOI":"10.1007\/s00521-019-04647-2","volume":"32","author":"Z Yang","year":"2019","unstructured":"Yang Z, Xie X, Zhan Q, Liu G, Cai Q (2019) A neural-network-based framework for cigarette laser code and identification. Neural Comput Appl 32(15):11597\u201311606","journal-title":"Neural Comput Appl"},{"key":"7005_CR22","unstructured":"Bochkovskiy A, Wang CY, Mark\u00a0Liao HY (2020). YOLOv4: optimal speed and accuracy of object detection. arXiv: 2004.10934v1, pp 1\u201317"},{"key":"7005_CR23","doi-asserted-by":"crossref","unstructured":"Kirillov A, He K, Girshick R, Rother C, Dollar P (2019) Panoptic segmentation. In: IEEE conference on computer vision and pattern recognition, pp 9396\u20139405","DOI":"10.1109\/CVPR.2019.00963"},{"key":"7005_CR24","doi-asserted-by":"crossref","unstructured":"Isola P, Zhu JY, Zhou T, Efros AA (2017). Image-to-image translation with conditional adversarial networks. In: IEEE international conference on computer vision, pp 5967\u20135976","DOI":"10.1109\/CVPR.2017.632"},{"key":"7005_CR25","doi-asserted-by":"crossref","unstructured":"Kim Y, Kim S, Kim T, Kim C (2019) CNN-based semantic segmentation using level set loss. In: 2019 IEEE winter conference on applications of computer vision (WACV), pp 1752\u20131760","DOI":"10.1109\/WACV.2019.00191"},{"key":"7005_CR26","doi-asserted-by":"crossref","unstructured":"Peng S, Jiang W, Pi H, Li X, Bao H, Zhou X (2020) Deep snake for real-time instance segmentation. arXiv:2001.01629, pp 1\u201310","DOI":"10.1109\/CVPR42600.2020.00856"},{"key":"7005_CR27","doi-asserted-by":"crossref","unstructured":"Chen G, Han K, Wong KKY (2018) TOM-Net: learning transparent object matting from a single image. In: 2018 IEEE\/CVF conference on computer vision and pattern recognition, pp 9233\u20139241","DOI":"10.1109\/CVPR.2018.00962"},{"issue":"5","key":"7005_CR28","first-page":"1","volume":"38","author":"Y Liang","year":"2020","unstructured":"Liang Y, Huang H, Cai Z, Hao Z, Feng F (2020) Survey of natural image matting. Appl Res Comput 38(5):1\u201310","journal-title":"Appl Res Comput"},{"key":"7005_CR29","unstructured":"Goodfellow IJ, Pouget-abadie J, Mirza M, Xu B, Warde-farley D (2014) Generative adversarial nets. arXiv: 1406.2661v1, pp 1\u20139"},{"key":"7005_CR30","unstructured":"Mirza M, Osindero S (2014) Conditional generative adversarial nets.pdf. arXiv: 1411.1784v1, pp 1\u20137"},{"key":"7005_CR31","unstructured":"Odena A, Olah C, Shlens J (2017) Conditional image synthesis with auxiliary classifier gans. In: International conference on machine learning, ICML 2017, vol\u00a06, pp 4043\u20134055"},{"key":"7005_CR32","unstructured":"Arjovsky M, Chintala S, Bottou L (2017) Wasserstein GAN. arXiv: 1701.07875v3, pp 1\u201332"},{"key":"7005_CR33","unstructured":"Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville A (2017) Improved training of Wasserstein GANs.pdf. arXiv: 1704.00028v3, pp 1\u201320"},{"issue":"1","key":"7005_CR34","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1109\/MSP.2017.2765202","volume":"35","author":"A Creswell","year":"2018","unstructured":"Creswell A, White T, Dumoulin V, Arulkumaran K, Sengupta B, Bharath AA (2018) Generative adversarial networks: an overview. IEEE Signal Process Mag 35(1):53\u201365","journal-title":"IEEE Signal Process Mag"},{"issue":"1","key":"7005_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3301282","volume":"52","author":"Y Hong","year":"2019","unstructured":"Hong Y, Hwang U, Yoo J, Yoon S (2019) How generative adversarial networks and their variants work: an overview. ACM Comput Surv 52(1):1\u201341","journal-title":"ACM Comput Surv"},{"key":"7005_CR36","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. arXiv: 1505.04597, pp 1\u20138","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"7005_CR37","unstructured":"Wang T, Liu M, Zhu J, Liu G, Tao A, Kautz J, Catanzaro B (2018) Video-to-video synthesis. arXiv: 1808.06601v2, pp 1\u201314"},{"key":"7005_CR38","doi-asserted-by":"crossref","unstructured":"Wang TC, Liu MY, Zhu JY, Tao A, Kautz J, Catanzaro B (2018) High-resolution image synthesis and semantic manipulation with conditional GANs. In: IEEE international conference on computer vision, pp 8798\u20138807","DOI":"10.1109\/CVPR.2018.00917"},{"key":"7005_CR39","doi-asserted-by":"crossref","unstructured":"Zhu JY, Park T, Isola P, Efros AA (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. In: IEEE international conference on computer vision, pp 2242\u20132251","DOI":"10.1109\/ICCV.2017.244"},{"key":"7005_CR40","doi-asserted-by":"crossref","unstructured":"Yi Z, Zhang H, Tan P, Gong M (2017) DualGAN: unsupervised dual learning for image-to-image translation. In: IEEE international conference on computer vision, pp 2868\u20132876","DOI":"10.1109\/ICCV.2017.310"},{"key":"7005_CR41","unstructured":"Kim T, Cha M, Kim H, Kwon J, Jiwon L (2017) Learning to discover cross-domain relations with generative adversarial networks. arXiv: 1703.05192, pp 1\u201310"},{"issue":"1","key":"7005_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1117\/1.JEI.29.1.013016","volume":"29","author":"B Li","year":"2020","unstructured":"Li B, Yuan X, Shi M (2020) Synthetic data generation based on local-foreground generative adversarial networks for surface defect detection. J Electron Imaging 29(1):1\u201314","journal-title":"J Electron Imaging"},{"key":"7005_CR43","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.neucom.2019.09.107","volume":"408","author":"L Xinyi","year":"2020","unstructured":"Xinyi L, Junhui M, Haodong Z, Boyu Z, Juntong X (2020) A learning-based approach for surface defect detection using small image datasets. Neurocomputing 408:112\u2013120","journal-title":"Neurocomputing"},{"key":"7005_CR44","doi-asserted-by":"publisher","first-page":"3388","DOI":"10.1109\/TIP.2019.2959741","volume":"29","author":"J Liu","year":"2020","unstructured":"Liu J, Wang C, Hai S, Bo D, Tao D (2020) Multistage GAN for fabric defect detection. IEEE Trans Image Process 29:3388\u20133400","journal-title":"IEEE Trans Image Process"},{"issue":"5","key":"7005_CR45","doi-asserted-by":"publisher","first-page":"2439","DOI":"10.1109\/TIM.2019.2954757","volume":"69","author":"Y Lyu","year":"2020","unstructured":"Lyu Y, Han Z, Zhong J, Li C, Liu Z (2020) A generic anomaly detection of catenary support components based on generative adversarial networks. IEEE Trans Instrum Meas 69(5):2439\u20132448","journal-title":"IEEE Trans Instrum Meas"},{"key":"7005_CR46","doi-asserted-by":"crossref","unstructured":"Wang J, Li X, Yang J (2018) Stacked conditional generative adversarial networks for jointly learning shadow detection and shadow removal. In: IEEE international conference on computer vision, pp 1788\u20131797","DOI":"10.1109\/CVPR.2018.00192"},{"key":"7005_CR47","doi-asserted-by":"crossref","unstructured":"Bolya D, Zhou C, Xiao F, Lee Y\u00a0J (2019) YOLACT: Real-time instance segmentation. arXiv:1904.02689, pp 1\u201312","DOI":"10.1109\/ICCV.2019.00925"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07005-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07005-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07005-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,25]],"date-time":"2022-06-25T09:16:59Z","timestamp":1656148619000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07005-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,26]]},"references-count":47,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["7005"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07005-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2022,2,26]]},"assertion":[{"value":"10 February 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 January 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 February 2022","order":3,"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 competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}