{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T21:33:43Z","timestamp":1775252023663,"version":"3.50.1"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,3,14]],"date-time":"2025-03-14T00:00:00Z","timestamp":1741910400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,14]],"date-time":"2025-03-14T00:00:00Z","timestamp":1741910400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-025-07135-8","type":"journal-article","created":{"date-parts":[[2025,3,14]],"date-time":"2025-03-14T20:21:52Z","timestamp":1741983712000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A vision-based inspection system for pharmaceutical production line"],"prefix":"10.1007","volume":"81","author":[{"given":"Haixia","family":"Xu","sequence":"first","affiliation":[]},{"given":"Yuting","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Kaiyu","family":"Hu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,14]]},"reference":[{"key":"7135_CR1","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 580\u2013587","DOI":"10.1109\/CVPR.2014.81"},{"key":"7135_CR2","doi-asserted-by":"crossref","unstructured":"Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp 1440\u20131448","DOI":"10.1109\/ICCV.2015.169"},{"key":"7135_CR3","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Doll\u00e1r 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"},{"key":"7135_CR4","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 779\u2013788","DOI":"10.1109\/CVPR.2016.91"},{"key":"7135_CR5","doi-asserted-by":"crossref","unstructured":"Redmon J, Farhadi A (2017) Yolo9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 7263\u20137271","DOI":"10.1109\/CVPR.2017.690"},{"key":"7135_CR6","volume-title":"Yolov3: An incremental improvement, (1804) 1\u20136","author":"A Farhadi","year":"2018","unstructured":"Farhadi A, Redmon J (2018) Yolov3: An incremental improvement, (1804) 1\u20136. Springer, Berlin"},{"key":"7135_CR7","unstructured":"Bochkovskiy A, Wang C-Y, Liao H-YM (2020) Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934"},{"key":"7135_CR8","doi-asserted-by":"publisher","unstructured":"Jocher G (2020) YOLOv5 by Ultralytics. https:\/\/doi.org\/10.5281\/zenodo.3908559. https:\/\/github.com\/ultralytics\/yolov5","DOI":"10.5281\/zenodo.3908559"},{"key":"7135_CR9","unstructured":"Li C, Li L, Jiang H, Weng K, Geng Y, Li L, Ke Z, Li Q, Cheng M, Nie W et al (2022) Yolov6: A single-stage object detection framework for industrial applications. arXiv preprint arXiv:2209.02976"},{"key":"7135_CR10","doi-asserted-by":"crossref","unstructured":"Wang C-Y, Bochkovskiy A, Liao H-YM (2023) Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 7464\u20137475","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"7135_CR11","unstructured":"Ge Z, Liu S, Wang F, Li Z, Sun J (2021) Yolox: Exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430"},{"key":"7135_CR12","unstructured":"Jocher G, Qiu J, Chaurasia A (2023) Ultralytics YOLO. https:\/\/github.com\/ultralytics\/ultralytics"},{"key":"7135_CR13","doi-asserted-by":"crossref","unstructured":"Wang C-Y, Yeh I-H, Mark\u00a0Liao H-Y (2025) Yolov9: Learning what you want to learn using programmable gradient information. In: European Conference on Computer Vision. Springer, pp 1\u201321","DOI":"10.1007\/978-3-031-72751-1_1"},{"issue":"4","key":"7135_CR14","doi-asserted-by":"publisher","first-page":"2189","DOI":"10.1109\/TII.2019.2935153","volume":"16","author":"X Zhou","year":"2019","unstructured":"Zhou X, Wang Y, Zhu Q, Mao J, Xiao C, Lu X, Zhang H (2019) A surface defect detection framework for glass bottle bottom using visual attention model and wavelet transform. IEEE Trans Industr Inf 16(4):2189\u20132201","journal-title":"IEEE Trans Industr Inf"},{"key":"7135_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105429","volume":"116","author":"Y Zhai","year":"2022","unstructured":"Zhai Y, Yang K, Zhao Z, Wang Q, Bai K (2022) Geometric characteristic learning r-cnn for shockproof hammer defect detection. Eng Appl Artif Intell 116:105429","journal-title":"Eng Appl Artif Intell"},{"key":"7135_CR16","doi-asserted-by":"crossref","unstructured":"Liu M, Chen Y, Xie J, He L, Zhang Y (2023) Lf-yolo: A lighter and faster yolo for weld defect detection of x-ray image, vol 23. IEEE, pp 7430\u20137439","DOI":"10.1109\/JSEN.2023.3247006"},{"key":"7135_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2023.112776","volume":"214","author":"C Zhao","year":"2023","unstructured":"Zhao C, Shu X, Yan X, Zuo X, Zhu F (2023) Rdd-yolo: a modified yolo for detection of steel surface defects. Measurement 214:112776","journal-title":"Measurement"},{"issue":"2","key":"7135_CR18","doi-asserted-by":"publisher","first-page":"95","DOI":"10.3390\/a16020095","volume":"16","author":"A Saberironaghi","year":"2023","unstructured":"Saberironaghi A, Ren J, El-Gindy M (2023) Defect detection methods for industrial products using deep learning techniques: a review. Algorithms 16(2):95","journal-title":"Algorithms"},{"key":"7135_CR19","doi-asserted-by":"publisher","first-page":"4284","DOI":"10.1109\/ACCESS.2021.3140118","volume":"10","author":"Q Liu","year":"2022","unstructured":"Liu Q, Wang C, Li Y, Gao M, Li J (2022) A fabric defect detection method based on deep learning. IEEE Access 10:4284\u20134296","journal-title":"IEEE Access"},{"key":"7135_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2019.102967","volume":"109","author":"X Yin","year":"2020","unstructured":"Yin X, Chen Y, Bouferguene A, Zaman H, Al-Hussein M, Kurach L (2020) A deep learning-based framework for an automated defect detection system for sewer pipes. Autom Constr 109:102967","journal-title":"Autom Constr"},{"key":"7135_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106390","volume":"123","author":"Z Zhang","year":"2023","unstructured":"Zhang Z, Zhou M, Wan H, Li M, Li G, Han D (2023) Idd-net: industrial defect detection method based on deep-learning. Eng Appl Artif Intell 123:106390","journal-title":"Eng Appl Artif Intell"},{"key":"7135_CR22","doi-asserted-by":"crossref","unstructured":"Liu X, Zhu Q, Wang Y, Zhou X, Li K, Liu X (2018) Machine vision based defect detection system for oral liquid vial. In: 2018 13th World Congress on Intelligent Control and Automation (WCICA). IEEE, pp 945\u2013950","DOI":"10.1109\/WCICA.2018.8630441"},{"key":"7135_CR23","unstructured":"Tiong LCO, Yoo HJ, Kim NY, Lee K-Y, Han SS, Kim D (2022) Machine vision for vial positioning detection toward the safe automation of material synthesis. arXiv preprint arXiv:2206.07272"},{"issue":"12","key":"7135_CR24","doi-asserted-by":"publisher","first-page":"8537","DOI":"10.1007\/s11760-024-03474-w","volume":"18","author":"H Xu","year":"2024","unstructured":"Xu H, Ding F, Zhou W, Han F, Liu Y, Zhu J (2024) Cff-yolo: cross-space feature fusion based yolo model for screw detection in vehicle chassis. SIViP 18(12):8537\u20138546","journal-title":"SIViP"},{"key":"7135_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2024.114970","volume":"235","author":"H Xu","year":"2024","unstructured":"Xu H, Han F, Zhou W, Liu Y, Ding F, Zhu J (2024) Esmnet: an enhanced yolov7-based approach to detect surface defects in precision metal workpieces. Measurement 235:114970","journal-title":"Measurement"},{"key":"7135_CR26","unstructured":"Yu F, Koltun V (2015) Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122"},{"key":"7135_CR27","doi-asserted-by":"crossref","unstructured":"Dai J, Qi H, Xiong Y, Li Y, Zhang G, Hu H, Wei Y (2017) Deformable convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp 764\u2013773","DOI":"10.1109\/ICCV.2017.89"},{"key":"7135_CR28","doi-asserted-by":"crossref","unstructured":"Qi Y, He Y, Qi X, Zhang Y, Yang G (2023) Dynamic snake convolution based on topological geometric constraints for tubular structure segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 6070\u20136079","DOI":"10.1109\/ICCV51070.2023.00558"},{"key":"7135_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2024.105190","volume":"149","author":"X Zhang","year":"2024","unstructured":"Zhang X, Song Y, Song T, Yang D, Ye Y, Zhou J, Zhang L (2024) Ldconv: linear deformable convolution for improving convolutional neural networks. Image Vis Comput 149:105190","journal-title":"Image Vis Comput"},{"key":"7135_CR30","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"7135_CR31","doi-asserted-by":"crossref","unstructured":"Gao Z, Xie J, Wang Q, Li P (2019) Global second-order pooling convolutional networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 3024\u20133033","DOI":"10.1109\/CVPR.2019.00314"},{"key":"7135_CR32","doi-asserted-by":"crossref","unstructured":"Wang X, Girshick R, Gupta A, He K (2018) Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 7794\u20137803","DOI":"10.1109\/CVPR.2018.00813"},{"key":"7135_CR33","doi-asserted-by":"crossref","unstructured":"Huang Z, Wang X, Huang L, Huang C, Wei Y, Liu W (2019) Ccnet: Criss-cross attention for semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 603\u2013612","DOI":"10.1109\/ICCV.2019.00069"},{"key":"7135_CR34","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J-Y, Kweon IS (2018) Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"7135_CR35","doi-asserted-by":"crossref","unstructured":"Pan X, Ge C, Lu R, Song S, Chen G, Huang Z, Huang G (2022) On the integration of self-attention and convolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 815\u2013825","DOI":"10.1109\/CVPR52688.2022.00089"},{"key":"7135_CR36","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Doll\u00e1r P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2117\u20132125","DOI":"10.1109\/CVPR.2017.106"},{"key":"7135_CR37","doi-asserted-by":"crossref","unstructured":"Zhao H, Shi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2881\u20132890","DOI":"10.1109\/CVPR.2017.660"},{"key":"7135_CR38","doi-asserted-by":"crossref","unstructured":"Li Y, Chen Y, Wang N, Zhang Z (2019) Scale-aware trident networks for object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 6054\u20136063","DOI":"10.1109\/ICCV.2019.00615"},{"key":"7135_CR39","unstructured":"Cognex: VisionPro Software (2024)"},{"key":"7135_CR40","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) Ssd: Single shot multibox detector. In: Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part I 14. Springer, pp 21\u201337","DOI":"10.1007\/978-3-319-46448-0_2"},{"issue":"6","key":"7135_CR41","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2016","unstructured":"Ren S, He K, Girshick R, Sun J (2016) 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"},{"key":"7135_CR42","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Goyal P, Girshick R, He K, Doll\u00e1r P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp 2980\u20132988","DOI":"10.1109\/ICCV.2017.324"},{"key":"7135_CR43","doi-asserted-by":"crossref","unstructured":"Tan M, Pang R, Le QV (2020) Efficientdet: Scalable and efficient object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 10781\u201310790","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"7135_CR44","doi-asserted-by":"crossref","unstructured":"Zhao Y, Lv W, Xu S, Wei J, Wang G, Dang Q, Liu Y, Chen J (2024) Detrs beat yolos on real-time object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 16965\u201316974","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"7135_CR45","unstructured":"Jocher G, Qiu J (2024) Ultralytics YOLO11. https:\/\/github.com\/ultralytics\/ultralytics"},{"issue":"4","key":"7135_CR46","doi-asserted-by":"publisher","first-page":"1922","DOI":"10.1109\/TPAMI.2020.3032166","volume":"44","author":"Z Tian","year":"2022","unstructured":"Tian Z, Shen C, Chen H, He T (2022) Fcos: a simple and strong anchor-free object detector. IEEE Trans Pattern Anal Mach Intell 44(4):1922\u20131933. https:\/\/doi.org\/10.1109\/TPAMI.2020.3032166","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"7135_CR47","first-page":"107984","volume":"37","author":"A Wang","year":"2025","unstructured":"Wang A, Chen H, Liu L, Chen K, Lin Z, Han J et al (2025) Yolov10: real-time end-to-end object detection. Adv Neural Inf Process Syst 37:107984\u2013108011","journal-title":"Adv Neural Inf Process Syst"},{"key":"7135_CR48","doi-asserted-by":"crossref","unstructured":"Duan K, Bai S, Xie L, Qi H, Huang Q, Tian Q (2019) Centernet: Keypoint triplets for object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV)","DOI":"10.1109\/ICCV.2019.00667"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07135-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-07135-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07135-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,14]],"date-time":"2025-03-14T20:22:02Z","timestamp":1741983722000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-07135-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,14]]},"references-count":48,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["7135"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-07135-8","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,14]]},"assertion":[{"value":"26 February 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 March 2025","order":2,"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":"Conflict of interest"}}],"article-number":"625"}}