{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T16:14:21Z","timestamp":1778775261429,"version":"3.51.4"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T00:00:00Z","timestamp":1760745600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T00:00:00Z","timestamp":1760745600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Interdisciplinary project of Dalian University","award":["DLUXK-2023-ZD-001"],"award-info":[{"award-number":["DLUXK-2023-ZD-001"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-025-07958-5","type":"journal-article","created":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T18:40:41Z","timestamp":1760812841000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Laser weld spot detection based on MMG-YOLO"],"prefix":"10.1007","volume":"81","author":[{"given":"Jianxin","family":"Feng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyu","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiahao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiguo","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanming","family":"Ding","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,18]]},"reference":[{"key":"7958_CR1","doi-asserted-by":"publisher","first-page":"120193","DOI":"10.1109\/ACCESS.2021.3108462","volume":"9","author":"Y Gao","year":"2021","unstructured":"Gao Y, Zhong P, Tang X (2021) Feature extraction of laser welding pool image and application in welding quality identification. IEEE Access 9:120193\u2013120202","journal-title":"IEEE Access"},{"key":"7958_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2023.3277935","volume":"72","author":"F Ulger","year":"2023","unstructured":"Ulger F, Yuksel SE, Yilmaz A (2023) Solder joint inspection on printed circuit boards: a survey and a dataset. IEEE Trans Instrum Meas 72:1\u201321","journal-title":"IEEE Trans Instrum Meas"},{"key":"7958_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.ultras.2021.106567","volume":"118","author":"L Ding","year":"2022","unstructured":"Ding L, Lu Q, Liu S (2022) Quality inspection of micro solder joints in laser spot welding by laser ultrasonic method. Ultrasonics 118:106567","journal-title":"Ultrasonics"},{"key":"7958_CR4","first-page":"12993","volume":"34","author":"Z Zheng","year":"2020","unstructured":"Zheng Z, Wang P, Liu W (2020) Distance-iou loss: faster and better learning for bounding box regression. Proc AAAI Conf Artif Intell 34:12993\u201313000","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"7958_CR5","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R (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":"7958_CR6","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":"7958_CR7","unstructured":"Redmon J, Farhadi A (2018) Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767"},{"key":"7958_CR8","unstructured":"Bochkovskiy A (2020) Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934"},{"key":"7958_CR9","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D (2016) Ssd: single shot multibox detector. In: Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part I, pp 21\u201337","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"7958_CR10","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T (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":"7958_CR11","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":"7958_CR12","unstructured":"Ren S, He K, Girshick R (2015) Faster r-cnn: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst 28"},{"key":"7958_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2022.104568","volume":"143","author":"C Kim","year":"2022","unstructured":"Kim C, Hwang S, Sohn H (2022) Weld crack detection and quantification using laser thermography, mask r-cnn, and cyclegan. Autom Constr 143:104568","journal-title":"Autom Constr"},{"issue":"6","key":"7958_CR14","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1016\/j.ndteint.2011.05.003","volume":"44","author":"X Lu","year":"2011","unstructured":"Lu X, Liao G, Zha Z (2011) A novel approach for flip chip solder joint inspection based on pulsed phase thermography. NDT & E Int 44(6):484\u2013489","journal-title":"NDT & E Int"},{"key":"7958_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.ndteint.2024.103048","volume":"143","author":"K Li","year":"2024","unstructured":"Li K, Xu L, Su L (2024) X-ray detection of ceramic packaging chip solder defects based on improved yolov5. NDT & E Int 143:103048","journal-title":"NDT & E Int"},{"issue":"2","key":"7958_CR16","doi-asserted-by":"publisher","first-page":"1525","DOI":"10.1007\/s40747-021-00600-w","volume":"8","author":"Q Zhang","year":"2022","unstructured":"Zhang Q, Zhang M, Gamanayake C (2022) Deep learning based solder joint defect detection on industrial printed circuit board x-ray images. Compl Intell Syst 8(2):1525\u20131537","journal-title":"Compl Intell Syst"},{"issue":"12","key":"7958_CR17","doi-asserted-by":"publisher","first-page":"2995","DOI":"10.1016\/j.microrel.2012.07.018","volume":"52","author":"RSH Yang","year":"2012","unstructured":"Yang RSH, Braden DR, Zhang GM (2012) An automated ultrasonic inspection approach for flip chip solder joint assessment. Microelectron Reliab 52(12):2995\u20133001","journal-title":"Microelectron Reliab"},{"key":"7958_CR18","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1016\/j.neucom.2022.02.077","volume":"488","author":"H Li","year":"2022","unstructured":"Li H, Hao K, Wei B (2022) A reliable solder joint inspection method based on a light-weight point cloud network and modulated loss. Neurocomputing 488:315\u2013327","journal-title":"Neurocomputing"},{"issue":"11","key":"7958_CR19","doi-asserted-by":"publisher","first-page":"1890","DOI":"10.1109\/TCPMT.2022.3224997","volume":"12","author":"S Liao","year":"2022","unstructured":"Liao S, Huang C, Liang Y (2022) Solder joint defect inspection method based on convnext-yolox. IEEE Trans Compon Packaging Manuf Technol 12(11):1890\u20131898","journal-title":"IEEE Trans Compon Packaging Manuf Technol"},{"key":"7958_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.ndteint.2022.102764","volume":"133","author":"C Ji","year":"2023","unstructured":"Ji C, Wang H, Li H (2023) Defects detection in weld joints based on visual attention and deep learning. NDT & E Int 133:102764","journal-title":"NDT & E Int"},{"issue":"6","key":"7958_CR21","doi-asserted-by":"publisher","first-page":"6098","DOI":"10.1109\/JSEN.2022.3147489","volume":"22","author":"L Yang","year":"2022","unstructured":"Yang L, Fan J, Huo B (2022) Image denoising of seam images with deep learning for laser vision seam tracking. IEEE Sens J 22(6):6098\u20136107","journal-title":"IEEE Sens J"},{"key":"7958_CR22","doi-asserted-by":"crossref","unstructured":"Li A, Hamzah R, Rahim SKNA (2024) Yolo algorithm with hybrid attention feature pyramid network for solder joint defect detection. IEEE Trans Compon Packaging Manuf Technol","DOI":"10.1109\/TCPMT.2024.3409773"},{"key":"7958_CR23","first-page":"1","volume":"71","author":"N Zeng","year":"2022","unstructured":"Zeng N, Wu P, Wang Z (2022) A small-sized object detection oriented multi-scale feature fusion approach with application to defect detection. IEEE Trans Instrum Meas 71:1\u201314","journal-title":"IEEE Trans Instrum Meas"},{"key":"7958_CR24","first-page":"1","volume":"72","author":"GQ Wang","year":"2023","unstructured":"Wang GQ, Zhang CZ, Chen MS (2023) Yolo-msapf: Multiscale alignment fusion with parallel feature filtering model for high accuracy weld defect detection. IEEE Trans Instrum Meas 72:1\u201314","journal-title":"IEEE Trans Instrum Meas"},{"key":"7958_CR25","doi-asserted-by":"crossref","unstructured":"Lin TY, Doll\u00e1r P, Girshick R (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":"7958_CR26","doi-asserted-by":"crossref","unstructured":"Wang K, Liew JH, Zou Y (2019) Panet: Few-shot image semantic segmentation with prototype alignment. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 9197\u20139206","DOI":"10.1109\/ICCV.2019.00929"},{"key":"7958_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2024.105057","volume":"147","author":"M Kang","year":"2024","unstructured":"Kang M, Ting CM, Ting FF (2024) Asf-yolo: a novel yolo model with attentional scale sequence fusion for cell instance segmentation. Image Vis Comput 147:105057","journal-title":"Image Vis Comput"},{"key":"7958_CR28","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee JY (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":"7958_CR29","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":"7958_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106442","volume":"123","author":"D Wan","year":"2023","unstructured":"Wan D, Lu R, Shen S (2023) Mixed local channel attention for object detection. Eng Appl Artif Intell 123:106442","journal-title":"Eng Appl Artif Intell"},{"key":"7958_CR31","unstructured":"Vaswani A (2017) Attention is all you need. arXiv preprint arXiv:1706.03762"},{"key":"7958_CR32","doi-asserted-by":"crossref","unstructured":"Han K, Wang Y, Tian Q (2020) Ghostnet: more features from cheap operations. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 1580\u20131589","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"7958_CR33","doi-asserted-by":"crossref","unstructured":"Zhang X, Zhou X, Lin M (2018) Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 6848\u20136856","DOI":"10.1109\/CVPR.2018.00716"},{"issue":"4","key":"7958_CR34","doi-asserted-by":"publisher","first-page":"1773","DOI":"10.1007\/s00521-023-09187-4","volume":"36","author":"E Elfatimi","year":"2024","unstructured":"Elfatimi E, Eryigit R, Shehu HA (2024) Impact of datasets on the effectiveness of mobilenet for beans leaf disease detection. Neural Comput Appl 36(4):1773\u20131789","journal-title":"Neural Comput Appl"},{"key":"7958_CR35","doi-asserted-by":"crossref","unstructured":"Koonce B, Koonce B (2021) EfficientNet, pp 109\u2013123","DOI":"10.1007\/978-1-4842-6168-2_10"},{"key":"7958_CR36","doi-asserted-by":"crossref","unstructured":"Liu Z, Chen Y, Gao Y (2024) Rotating-yolo: a novel yolo model for remote sensing rotating object detection. Image Vis Comput 105397","DOI":"10.1016\/j.imavis.2024.105397"},{"key":"7958_CR37","doi-asserted-by":"crossref","unstructured":"Rezatofighi H, Tsoi N, Gwak JY (2019) Generalized intersection over union: a metric and a loss for bounding box regression. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 658\u2013666","DOI":"10.1109\/CVPR.2019.00075"},{"issue":"8","key":"7958_CR38","doi-asserted-by":"publisher","first-page":"8574","DOI":"10.1109\/TCYB.2021.3095305","volume":"52","author":"Z Zheng","year":"2021","unstructured":"Zheng Z, Wang P, Ren D (2021) Enhancing geometric factors in model learning and inference for object detection and instance segmentation. IEEE Trans Cybern 52(8):8574\u20138586","journal-title":"IEEE Trans Cybern"},{"key":"7958_CR39","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.neucom.2022.07.042","volume":"506","author":"YF Zhang","year":"2022","unstructured":"Zhang YF, Ren W, Zhang Z (2022) Focal and efficient iou loss for accurate bounding box regression. Neurocomputing 506:146\u2013157","journal-title":"Neurocomputing"},{"key":"7958_CR40","unstructured":"Gevorgyan Z (2022) Siou loss: More powerful learning for bounding box regression. arXiv preprint arXiv:2205.12740"},{"key":"7958_CR41","unstructured":"Zhang H, Zhang S (2024) Focaler-iou: More focused intersection over union loss. arXiv preprint arXiv:2401.10525"},{"key":"7958_CR42","unstructured":"Zhang H, Xu C, Zhang S (2023) Inner-iou: more effective intersection over union loss with auxiliary bounding box. arXiv preprint arXiv:2311.02877"},{"key":"7958_CR43","doi-asserted-by":"crossref","unstructured":"Wang CY, Yeh IH, Liao HYM (2024) Yolov9: learning what you want to learn using programmable gradient information. arXiv preprint arXiv:2402.13616","DOI":"10.1007\/978-3-031-72751-1_1"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07958-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-07958-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07958-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T18:40:47Z","timestamp":1760812847000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-07958-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,18]]},"references-count":43,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["7958"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-07958-5","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,18]]},"assertion":[{"value":"19 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 October 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 Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"1478"}}