{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T17:50:24Z","timestamp":1773683424709,"version":"3.50.1"},"reference-count":41,"publisher":"Tech Science Press","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2025]]},"DOI":"10.32604\/cmc.2025.066152","type":"journal-article","created":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T02:05:48Z","timestamp":1749434748000},"page":"1397-1415","source":"Crossref","is-referenced-by-count":1,"title":["Deep Learning-Based Glass Detection for Smart Glass Manufacturing Processes"],"prefix":"10.32604","volume":"84","author":[{"given":"Seungmin","family":"Lee","sequence":"first","affiliation":[]},{"given":"Beomseong","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Heesung","family":"Lee","sequence":"additional","affiliation":[]}],"member":"17807","published-online":{"date-parts":[[2025]]},"reference":[{"key":"ref1","series-title":"2014 IEEE International Conference on Industrial Engineering and Engineering Management","first-page":"697","article-title":"Smart factories in Industry 4.0: a review of the concept and of energy management approached in production based on the Internet of Things paradigm","author":"Shrouf","year":"2014 Dec 9\u201312"},{"key":"ref2","first-page":"100017","article-title":"Digital twin for smart manufacturing, a review","volume":"2","author":"Soori","year":"2023","journal-title":"Sustain Manufac Service Econom"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/s40684-016-0015-5","article-title":"Smart manufacturing: past research, present findings, and future directions","volume":"3","author":"Kang","year":"2016","journal-title":"Int J Prec Eng Manuf-green Technol"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.jmsy.2018.01.006","article-title":"Data-driven smart manufacturing","volume":"48","author":"Tao","year":"2018","journal-title":"J Manuf Syst"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"570","DOI":"10.3390\/mi14030570","article-title":"Artificial intelligence-based smart quality inspection for manufacturing","volume":"14","author":"Sundaram","year":"2023","journal-title":"Micromachines"},{"key":"ref6","series-title":"2020 9th International Conference on Industrial Technology and Management (ICITM)","first-page":"127","article-title":"Artificial intelligence for product quality inspection toward smart industries: quality control of vehicle non-conformities","author":"Chouchene","year":"2020"},{"key":"ref7","first-page":"39","article-title":"Quality monitoring during the installation of large tempered glass structures","volume":"84","author":"Yatsuk","year":"2023","journal-title":"Meas Equip Metro"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1016\/j.matdes.2007.03.022","article-title":"The tempering of glass and the failure of tempered glass plates with pin-loaded joints: modelling and simulation","volume":"29","author":"To","year":"2008","journal-title":"Mater Des"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1111\/j.1151-2916.1968.tb11840.x","article-title":"Fracture of tempered glass","volume":"51","author":"Barsom","year":"1968","journal-title":"J Am Ceram Soc"},{"key":"ref10","series-title":"Proceedings of the 49th Conference on Glass Problems: Ceramic Engineering and Science Proceedings","first-page":"193","article-title":"Fundamentals of tempered glass","author":"McMaster","year":"1989"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"04020043","DOI":"10.1061\/(ASCE)MT.1943-5533.0003086","article-title":"Nondestructive safety evaluation of thermally tempered glass","volume":"32","author":"Zaccaria","year":"2020","journal-title":"J Mater Civ Eng"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"1577","DOI":"10.1007\/s00170-022-10649-7","article-title":"Online prediction of automotive tempered glass quality using machine learning","volume":"125","author":"Khdoudi","year":"2023","journal-title":"Int J Adv Manuf Technol"},{"key":"ref13","series-title":"China Automation Congress (CAC)","first-page":"3715","article-title":"A method for surface defect detection of tempered glass based on polarization characteristics and unsupervised learning","author":"Weiqi","year":"2024"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"914","DOI":"10.1080\/01495739.2019.1590169","article-title":"Online and real-time accurate prediction of tempered glass surface stress during quenching process","volume":"42","author":"Yue","year":"2019","journal-title":"J Therm Stress"},{"key":"ref15","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"3687","article-title":"Don\u2019t Hit Me! glass detection in real-world scenes","volume":"2020","author":"Mei","year":"2020"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1111\/cgf.14441","article-title":"GlassNet: label decoupling-based three-stream neural network for robust image glass detection","volume":"41","author":"Zheng","year":"2022","journal-title":"Comput Graph Forum"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1109\/TPAMI.2024.3463490","article-title":"GhostingNet: a novel approach for glass surface detection with ghosting cues","volume":"47","author":"Yan","year":"2025","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref18","doi-asserted-by":"crossref","unstructured":"Han D, Lee S, Zhang C, Yoon H, Kwon H, Kim HC, et al. Internal-external boundary attention fusion for glass surface segmentation. arXiv:2307.00212. 2023. doi:10.48550\/arXiv.2307.00212.","DOI":"10.2139\/ssrn.5021486"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"1783","DOI":"10.1016\/S0031-3203(98)00178-2","article-title":"Learning affine transformations","volume":"32","author":"Bebis","year":"1999","journal-title":"Patt Recogn"},{"key":"ref20","doi-asserted-by":"crossref","first-page":"6217","DOI":"10.1109\/TCYB.2020.3036393","article-title":"Affine transformation-enhanced multifactorial optimization for heterogeneous problems","volume":"52","author":"Xue","year":"2020","journal-title":"IEEE Trans Cyber"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"3321","DOI":"10.1109\/TIP.2021.3060803","article-title":"Affine transformation-based deep frame prediction","volume":"30","author":"Choi","year":"2021","journal-title":"IEEE Trans Image Process"},{"key":"ref22","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1007\/978-0-387-73003-5_196","article-title":"Gaussian mixture models","volume":"741","author":"Reynolds","year":"2009","journal-title":"Encyclo Biomet"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"1055","DOI":"10.1111\/1468-0262.00151","article-title":"GMM with weak identification","volume":"68","author":"Stock","year":"2000","journal-title":"Econometrica"},{"key":"ref24","volume":"12","author":"Rasmussen","year":"1999","journal-title":"Advances in neural information processing systems"},{"key":"ref25","series-title":"European Conference on Computer Vision","first-page":"1","article-title":"YOLOv9: learning what you want to learn using programmable gradient information","author":"Wang","year":"2025"},{"key":"ref26","first-page":"107984","article-title":"Yolov10: real-time end-to-end object detection","volume":"37","author":"Wang","year":"2024","journal-title":"Adv Neural Inform Process Syst"},{"key":"ref27","unstructured":"Jocher G, Qiu J. Ultralytics YOLO11. Version 11.0.0. 2024. [cited 2025 May 15]. Available from: https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"ref28","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"7464","article-title":"YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors","author":"Wang","year":"2023"},{"key":"ref29","series-title":"IPPR Conference on Computer Vision, Graphics, and Image Processing","article-title":"Transparent object detection using regions with convolutional neural network","author":"Lai","year":"2015"},{"key":"ref30","doi-asserted-by":"crossref","first-page":"122717","DOI":"10.1016\/j.conbuildmat.2021.122717","article-title":"Image-based surface scratch detection on architectural glass panels using deep learning approach","volume":"282","author":"Pan","year":"2021","journal-title":"Constr Build Mater"},{"key":"ref31","doi-asserted-by":"crossref","first-page":"101346","DOI":"10.1016\/j.rineng.2023.101346","article-title":"Design of automated system for online inspection using the convolutional neural network (CNN) technique in the image processing approach","volume":"19","author":"Ngo","year":"2023","journal-title":"Results Eng"},{"key":"ref32","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.wasman.2021.12.001","article-title":"Deep learning-based waste detection in natural and urban environments","volume":"138","author":"Majchrowska","year":"2022","journal-title":"Waste Manag"},{"key":"ref33","doi-asserted-by":"crossref","first-page":"103661","DOI":"10.1016\/j.compind.2022.103661","article-title":"Deep learning-based object detection in augmented reality: a systematic review","volume":"139","author":"Ghasemi","year":"2022","journal-title":"Comput Ind"},{"key":"ref34","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.mfglet.2019.12.006","article-title":"Advanced cover glass defect detection and classification based on multi-DNN model","volume":"23","author":"Park","year":"2020","journal-title":"Manuf Lett"},{"key":"ref35","article-title":"Inspection system for glass bottle defect classification based on deep neural network","volume":"14","author":"Claypo","year":"2023","journal-title":"Int J Adv Comput Sci Appl"},{"key":"ref36","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1109\/TPAMI.2010.161","article-title":"Contour detection and hierarchical image segmentation","volume":"33","author":"Arbelaez","year":"2010","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref37","series-title":"IEEE\/CVF International Conference on Computer Vision","first-page":"16624","article-title":"Learning semi-supervised gaussian mixture models for generalized category discovery","author":"Zhao","year":"2023 Oct 26"},{"key":"ref38","first-page":"20","article-title":"Application of EM algorithm in classification problem & parameter estimation of gaussian mixture model","volume":"13","author":"Nwobi","year":"2023","journal-title":"Int J Stat Appl"},{"key":"ref39","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1145\/3687251.3687252","article-title":"Adapting gaussian mixture model training to Embedded\/Edge devices: a Low I\/O, deadline-aware and energy efficient design","volume":"24","author":"Bouzouad","year":"2024","journal-title":"ACM SIGAPP Appl Comput Review"},{"key":"ref40","unstructured":"Jani M, Fayyad J, Al-Younes Y, Najjaran H. Model compression methods for YOLOv5: a review. arXiv:2307.11904. 2023. doi:10.48550\/arxiv.2307.11904."},{"key":"ref41","unstructured":"Nwankpa C, Ijomah W, Gachagan A, Marshall S. Activation functions: comparison of trends in practice and research for deep learning. arXiv:1811.03378. 2018. doi:10.48550\/arxiv.1811.03378."}],"container-title":["Computers, Materials &amp; Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cdn.techscience.cn\/files\/cmc\/2025\/TSP_CMC-84-1\/TSP_CMC_66152\/TSP_CMC_66152.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T01:37:51Z","timestamp":1763343471000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v84n1\/61789"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":41,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2025.066152","relation":{},"ISSN":["1546-2226"],"issn-type":[{"value":"1546-2226","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}