{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T05:10:04Z","timestamp":1749273004385,"version":"3.41.0"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"5-6","license":[{"start":{"date-parts":[[2025,1,25]],"date-time":"2025-01-25T00:00:00Z","timestamp":1737763200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,25]],"date-time":"2025-01-25T00:00:00Z","timestamp":1737763200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No. 51705053"],"award-info":[{"award-number":["No. 51705053"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Chongqing Talents Program Innovation and Entrepreneurship Demonstration Team","award":["No. CSTC2021ycjh-bgzxm0346"],"award-info":[{"award-number":["No. CSTC2021ycjh-bgzxm0346"]}]},{"DOI":"10.13039\/501100013223","name":"Chongqing Research Program of Basic Research and Frontier Technology","doi-asserted-by":"publisher","award":["No. CSTB2022nscq-LZX0052"],"award-info":[{"award-number":["No. CSTB2022nscq-LZX0052"]}],"id":[{"id":"10.13039\/501100013223","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Research Program of Chongqing Municipal Education Commission","award":["No. KJZD-M202201501"],"award-info":[{"award-number":["No. KJZD-M202201501"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s13042-024-02459-y","type":"journal-article","created":{"date-parts":[[2025,1,25]],"date-time":"2025-01-25T06:52:23Z","timestamp":1737787943000},"page":"3413-3428","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Faster and lighter surface defect detection model for transparent wine bottle"],"prefix":"10.1007","volume":"16","author":[{"given":"Zhongliang","family":"Lv","sequence":"first","affiliation":[]},{"given":"Guojun","family":"Gu","sequence":"additional","affiliation":[]},{"given":"Kewen","family":"Xia","sequence":"additional","affiliation":[]},{"given":"Zhiqiang","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Kang","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Yin","sequence":"additional","affiliation":[]},{"given":"Luanfeng","family":"Xing","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Ying","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,25]]},"reference":[{"key":"2459_CR1","doi-asserted-by":"crossref","unstructured":"Latina MAE, John Van Russel R, Santos FDD (2022) Empty Glass Bottle Defect Detection Based on Deep Learning with CNN Using SSD MobileNetV2 Model[C]\/\/2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM). IEEE, : 1\u20136","DOI":"10.1109\/HNICEM57413.2022.10109368"},{"key":"2459_CR2","doi-asserted-by":"publisher","first-page":"116710","DOI":"10.1016\/j.eswa.2022.116710","volume":"198","author":"E Versini","year":"2022","unstructured":"Versini E, Snidaro L, Liani A (2022) SCL\u2014Segmentation\u2013classification combined loss for surface defect detection[J]. Expert Syst Appl 198:116710","journal-title":"Expert Syst Appl"},{"key":"2459_CR3","unstructured":"Pei J, Li S, Li Y Surface Defects Detection of Transparent Plastic Bottles Based on Improved Yolov5[J]. Available at SSRN 4524937"},{"key":"2459_CR4","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s40684-016-0039-x","volume":"3","author":"JK Park","year":"2016","unstructured":"Park JK, Kwon BK, Park JH et al (2016) Machine learning-based imaging system for surface defect inspection[J]. Int J Precision Eng Manufacturing-Green Technol 3:303\u2013310","journal-title":"Int J Precision Eng Manufacturing-Green Technol"},{"issue":"4","key":"2459_CR5","doi-asserted-by":"publisher","first-page":"1995","DOI":"10.1007\/s10845-021-01878-w","volume":"34","author":"SA Singh","year":"2023","unstructured":"Singh SA, Desai KA (2023) Automated surface defect detection framework using machine vision and convolutional neural networks[J]. J Intell Manuf 34(4):1995\u20132011","journal-title":"J Intell Manuf"},{"key":"2459_CR6","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1016\/j.jmapro.2021.06.018","volume":"68","author":"M Eshkevari","year":"2021","unstructured":"Eshkevari M, Rezaee MJ, Zarinbal M et al (2021) Automatic dimensional defect detection for glass vials based on machine vision: a heuristic segmentation method[J]. J Manuf Process 68:973\u2013989","journal-title":"J Manuf Process"},{"key":"2459_CR7","doi-asserted-by":"publisher","first-page":"1437","DOI":"10.3390\/e23111437","volume":"23","author":"T Bai","year":"2021","unstructured":"Bai T, Gao J, Yang J, Yao DA (2021) Study on Railway Surface defects Detection based on machine vision. Entropy 23:1437. https:\/\/doi.org\/10.3390\/e23111437","journal-title":"Entropy"},{"key":"2459_CR8","unstructured":"Bochkovskiy A, Wang CY, Liao HYM (2020) Yolov4: optimal speed and accuracy of object detection. arxiv preprint arxiv:2004.10934"},{"key":"2459_CR9","doi-asserted-by":"crossref","unstructured":"Koonce B, Koonce B (2021) MobileNetV3[J]. Convolutional neural networks with swift for tensorflow 125\u2013144","DOI":"10.1007\/978-1-4842-6168-2_11"},{"key":"2459_CR10","doi-asserted-by":"publisher","first-page":"5114","DOI":"10.3390\/s23115114","volume":"23","author":"K Xia","year":"2023","unstructured":"Xia K, Lv Z, Zhou C, Gu G, Zhao Z, Liu K, Li Z (2023) Mixed receptive fields augmented YOLO with Multi-path Spatial Pyramid Pooling for Steel Surface Defect Detection. Sensors 23:5114. https:\/\/doi.org\/10.3390\/s23115114","journal-title":"Sensors"},{"key":"2459_CR11","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R et al (2016) You only look once: Unified, real-time object detection[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. : 779\u2013788","DOI":"10.1109\/CVPR.2016.91"},{"key":"2459_CR12","doi-asserted-by":"publisher","unstructured":"Tan M, Pang R, Le QV. EfficientDet: Scalable and Efficient Object Detection[C]\/\/2020 IEEE\/CVF Conference on Computer Vision and, Recognition P (2020) (CVPR).IEEE, https:\/\/doi.org\/10.1109\/CVPR42600.2020.01079","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"2459_CR13","doi-asserted-by":"publisher","unstructured":"Lin M, Ma L, Yu B An Efficient and Light-weight Detector for Wine Bottle Defects*[C]\/\/2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV).2020.https:\/\/doi.org\/10.1109\/ICARCV50220.2020.9305489","DOI":"10.1109\/ICARCV50220.2020.9305489"},{"key":"2459_CR14","doi-asserted-by":"publisher","first-page":"928900","DOI":"10.3389\/fbioe.2022.928900","volume":"10","author":"G Gong","year":"2022","unstructured":"Gong G, Huang J, Wang H (2022) Flaw detection in White Porcelain wine bottles based on improved YOLOv4 algorithm. Front Bioeng Biotechnol 10:928900. https:\/\/doi.org\/10.3389\/fbioe.2022.928900PMID: 35898650; PMCID: PMC9309803","journal-title":"Front Bioeng Biotechnol"},{"key":"2459_CR15","doi-asserted-by":"publisher","unstructured":"Zhang X, Yan L, Yan H (2020). Defect detection of bottled liquor based on deep learning[J].CSAA\/IET International Conference on Aircraft Utility Systems (AUS 2020), 2020:1259\u20131264.https:\/\/doi.org\/10.1049\/icp.2021.0415","DOI":"10.1049\/icp.2021.0415"},{"key":"2459_CR16","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S et al (2016) Deep Residual Learning for Image Recognition[C]\/\/IEEE Conference on Computer Vision and Pattern Recognition.IEEE, https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"2459_CR17","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T et al (2014) Rich feature hierarchies for accurate object detection and semantic segmentation[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. : 580\u2013587","DOI":"10.1109\/CVPR.2014.81"},{"key":"2459_CR18","doi-asserted-by":"crossref","unstructured":"Girshick R (2015) Fast r-cnn[C]\/\/Proceedings of the IEEE international conference on computer vision. : 1440\u20131448","DOI":"10.1109\/ICCV.2015.169"},{"key":"2459_CR19","doi-asserted-by":"publisher","unstructured":"Ren S, He K, Girshick R et al Faster R-CNN: towards real-time object detection with Region Proposal Networks[C]\/\/NIPS.2016.DOI:https:\/\/doi.org\/10.1109\/tpami.2016.2577031","DOI":"10.1109\/tpami.2016.2577031"},{"key":"2459_CR20","doi-asserted-by":"publisher","unstructured":"Krizhevsky A, Sutskever I, Hinton G (2012) .ImageNet classification with deep convolutional neural Networks[J].Advances in neural information processing systems. 25(2). https:\/\/doi.org\/10.1145\/3065386","DOI":"10.1145\/3065386"},{"key":"2459_CR21","doi-asserted-by":"crossref","unstructured":"Wang H et al (2022) Plant disease detection and classification method based on the optimized lightweight YOLOv5 model. Agriculture 12(7):931","DOI":"10.3390\/agriculture12070931"},{"key":"2459_CR22","unstructured":"Jocher G (2020) YOLOv5 by ultralytics[J]. Released date 5\u201329"},{"issue":"23","key":"2459_CR23","doi-asserted-by":"publisher","first-page":"12312","DOI":"10.3390\/app122312312","volume":"12","author":"H Xu","year":"2022","unstructured":"Xu H, Li B, Zhong F (2022) Light-YOLOv5: a Lightweight Algorithm for Improved YOLOv5 in Complex Fire scenarios. Appl Sci 12(23):12312","journal-title":"Appl Sci"},{"key":"2459_CR24","doi-asserted-by":"crossref","unstructured":"Ye M, Wang H (2023) and Hang Xiao. Light-YOLOv5: A Lightweight Algorithm for Improved YOLOv5 in PCB Defect Detection. 2023 IEEE 2nd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA). IEEE","DOI":"10.1109\/EEBDA56825.2023.10090731"},{"key":"2459_CR25","doi-asserted-by":"crossref","unstructured":"Xu S et al (2022) An improved lightweight yolov5 model based on attention mechanism for face mask detection. Artificial Neural Networks and Machine Learning\u2013ICANN. : 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6\u20139, 2022, Proceedings, Part III. Cham: Springer Nature Switzerland, 2022","DOI":"10.1007\/978-3-031-15934-3_44"},{"issue":"18","key":"2459_CR26","doi-asserted-by":"publisher","first-page":"7088","DOI":"10.3390\/s22187088","volume":"22","author":"L Pang","year":"2022","unstructured":"Pang L et al (2022) A lightweight YOLOv5-MNE algorithm for SAR Ship Detection. Sensors 22(18):7088","journal-title":"Sensors"},{"key":"2459_CR27","doi-asserted-by":"crossref","unstructured":"Liu W et al (2021) Real-time signal light detection based on yolov5 for railway. IOP Conference Series: Earth and Environmental Science. Vol. 769. No. 4. IOP Publishing","DOI":"10.1088\/1755-1315\/769\/4\/042069"},{"key":"2459_CR28","doi-asserted-by":"crossref","unstructured":"Chen J et al (2023) Run, Don\u2019t Walk: Chasing Higher FLOPS for Faster Neural Networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","DOI":"10.1109\/CVPR52729.2023.01157"},{"key":"2459_CR29","doi-asserted-by":"publisher","unstructured":"Chollet FX (2017) Deep Learning with Depthwise Separable Convolutions[C]\/\/2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).IEEE, https:\/\/doi.org\/10.1109\/CVPR.2017.195","DOI":"10.1109\/CVPR.2017.195"},{"key":"2459_CR30","unstructured":"Redmon J, Farhadi A 2018 Yolov3: an incremental improvement (arXiv:1804.02767)"},{"key":"2459_CR31","doi-asserted-by":"crossref","unstructured":"Wang CY, Bochkovskiy A, Liao HYM (2023) YOLOv7 : trainable bag-of-freebies sets new state-of-the-art for real-time object detectors Proc. IEEE\/CVF Conf. on Computer Vision and Pattern Recognition pp 7464\u201375","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"2459_CR32","doi-asserted-by":"crossref","unstructured":"Duan K, Bai S, Xie L et al (2019) Centernet: Keypoint triplets for object detection[C]\/\/Proceedings of the IEEE\/CVF international conference on computer vision. : 6569\u20136578","DOI":"10.1109\/ICCV.2019.00667"},{"key":"2459_CR33","doi-asserted-by":"crossref","unstructured":"Zhang S, Chi C, Yao Y, Lei Z, Li SZ (2019) Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection Proc. IEEE\/CVF Conf. on Computer Vision and Pattern Recognition pp 9759\u201368","DOI":"10.1109\/CVPR42600.2020.00978"},{"key":"2459_CR34","unstructured":"Jocher G, Chaurasia A, Qiu J 2023 YOLO by ultralytics (https:\/\/github.com\/ultralytics\/ultralytics\/) (Accessed 6 July 2023)"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02459-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02459-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02459-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T04:33:28Z","timestamp":1749270808000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02459-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,25]]},"references-count":34,"journal-issue":{"issue":"5-6","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["2459"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02459-y","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2025,1,25]]},"assertion":[{"value":"12 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 January 2025","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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}