{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T18:30:54Z","timestamp":1771957854732,"version":"3.50.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T00:00:00Z","timestamp":1740355200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T00:00:00Z","timestamp":1740355200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the Key Projects for Postgraduate Students of Hunan Provincial Department of Education","award":["No.CX20210742"],"award-info":[{"award-number":["No.CX20210742"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-025-07045-9","type":"journal-article","created":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T07:32:09Z","timestamp":1740382329000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["ASTKD-PCB-LDD: high-performance PCB defect detection model with align soft-target knowledge distillation and lightweight network design"],"prefix":"10.1007","volume":"81","author":[{"given":"Zhelun","family":"Hu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shenbo","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongxue","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Longhao","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lijun","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,24]]},"reference":[{"issue":"7","key":"7045_CR1","doi-asserted-by":"publisher","first-page":"4692","DOI":"10.1109\/TIM.2019.2957866","volume":"69","author":"D-M Tsai","year":"2019","unstructured":"Tsai D-M, Chou Y-H (2019) Fast and precise positioning in PCBS using deep neural network regression. IEEE Trans Instrum Meas 69(7):4692\u20134701","journal-title":"IEEE Trans Instrum Meas"},{"key":"7045_CR2","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"},{"issue":"9","key":"7045_CR3","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"K He","year":"2015","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37(9):1904\u20131916","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"7045_CR4","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster r-CNN: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst 28"},{"key":"7045_CR5","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, 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, pp 21\u201337. Springer","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"7045_CR6","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":"7045_CR7","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":"7045_CR8","unstructured":"Redmon J, Farhadi A (2018) YOLO3: an incremental improvement. arXiv preprint arXiv:1804.02767"},{"key":"7045_CR9","unstructured":"Bochkovskiy A, Wang CY, Liao HYM (2020) YOLO4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934"},{"key":"7045_CR10","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":"7045_CR11","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. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition pp 7464\u20137475","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"7045_CR12","doi-asserted-by":"crossref","unstructured":"Adibhatla VA, Chih HC, Hsu CC, Cheng J, Abbod MF, Shieh JS (2021) Applying deep learning to defect detection in printed circuit boards via a newest model of you-only-look-once","DOI":"10.3390\/electronics9091547"},{"key":"7045_CR13","doi-asserted-by":"publisher","first-page":"129480","DOI":"10.1109\/ACCESS.2022.3228206","volume":"10","author":"W Chen","year":"2022","unstructured":"Chen W, Huang Z, Mu Q, Sun Y (2022) PCB defect detection method based on transformer-yolo. IEEE Access 10:129480\u2013129489","journal-title":"IEEE Access"},{"issue":"7","key":"7045_CR14","doi-asserted-by":"publisher","first-page":"5963","DOI":"10.3390\/su15075963","volume":"15","author":"J Tang","year":"2023","unstructured":"Tang J, Liu S, Zhao D, Tang L, Zou W, Zheng B (2023) PCB-YOLO: An improved detection algorithm of PCB surface defects based on YOLOv5. Sustainability 15(7):5963","journal-title":"Sustainability"},{"issue":"13","key":"7045_CR15","doi-asserted-by":"publisher","first-page":"2821","DOI":"10.3390\/electronics12132821","volume":"12","author":"B Du","year":"2023","unstructured":"Du B, Wan F, Lei G, Xu L, Xu C, Xiong Y (2023) YOLO-MBBi: PCB surface defect detection method based on enhanced YOLOv5. Electronics 12(13):2821","journal-title":"Electronics"},{"key":"7045_CR16","doi-asserted-by":"crossref","unstructured":"Ye M, Wang H, Xiao H (2023) Light-YOLOv5: a lightweight algorithm for improved YOLOv5 in PCB defect detection. In: 2023 IEEE 2nd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA), pp. 523\u2013528. IEEE","DOI":"10.1109\/EEBDA56825.2023.10090731"},{"key":"7045_CR17","doi-asserted-by":"crossref","unstructured":"Li D, Xu A, Yu X (2023) Optimized lightweight PCB real-time defect detection algorithm. In: 2023 IEEE 16th International Conference on Electronic Measurement & Instruments (ICEMI) pp 262\u2013269. IEEE","DOI":"10.1109\/ICEMI59194.2023.10270749"},{"key":"7045_CR18","doi-asserted-by":"publisher","first-page":"130339","DOI":"10.1109\/ACCESS.2022.3227205","volume":"10","author":"X Qian","year":"2022","unstructured":"Qian X, Wang X, Yang S, Lei J (2022) LFF-YOLO: a YOLO algorithm with lightweight feature fusion network for multi-scale defect detection. IEEE Access 10:130339\u2013130349","journal-title":"IEEE Access"},{"issue":"1","key":"7045_CR19","first-page":"100","volume":"28","author":"JA Hartigan","year":"1979","unstructured":"Hartigan JA, Wong MA (1979) Algorithm as 136: a k-means clustering algorithm. J R Stat Soc Ser C Appl Stat 28(1):100\u2013108","journal-title":"J R Stat Soc Ser C Appl Stat"},{"key":"7045_CR20","doi-asserted-by":"crossref","unstructured":"Chen J, Kao SH, He H, Zhuo W, Wen S, Lee CH, Chan SHG (2023) Run, don\u2019t walk: chasing higher flops for faster neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition pp 12021\u201312031","DOI":"10.1109\/CVPR52729.2023.01157"},{"key":"7045_CR21","doi-asserted-by":"crossref","unstructured":"Han K, Wang Y, Tian Q, Guo J, Xu C, Xu C (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":"7045_CR22","unstructured":"Li H, Li J, Wei H, Liu Z, Zhan Z, Ren Q (2022) Slim-neck by GSConv: a better design paradigm of detector architectures for autonomous vehicles. arXiv preprint arXiv:2206.02424"},{"key":"7045_CR23","unstructured":"Gevorgyan Z (2022) SIoU loss: more powerful learning for bounding box regression. arXiv preprint arXiv:2205.12740"},{"key":"7045_CR24","doi-asserted-by":"crossref","unstructured":"Yang L, Zhou X, Li X, Qiao L, Li Z, Yang Z, Wang G, Li X (2023) Bridging cross-task protocol inconsistency for distillation in dense object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision pp 17175\u201317184","DOI":"10.1109\/ICCV51070.2023.01575"},{"key":"7045_CR25","unstructured":"Malinin A, Gales M (2019) Reverse KL-divergence training of prior networks: improved uncertainty and adversarial robustness. Adv Neural Inf Process Syst 32"},{"key":"7045_CR26","unstructured":"Lee J, Park S, Mo S, Ahn S, Shin J (2020) Layer-adaptive sparsity for the magnitude-based pruning. arXiv preprint arXiv:2010.07611"},{"issue":"2","key":"7045_CR27","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1049\/trit.2019.0019","volume":"4","author":"R Ding","year":"2019","unstructured":"Ding R, Dai L, Li G, Liu H (2019) TDD-net: a tiny defect detection network for printed circuit boards. CAAI Trans Intell Technol 4(2):110\u2013116","journal-title":"CAAI Trans Intell Technol"},{"key":"7045_CR28","doi-asserted-by":"crossref","unstructured":"Kisantal M, Wojna Z, Murawski J, Naruniec J, Cho, K (2019) Augmentation for small object detection. arXiv preprint arXiv:1902.07296","DOI":"10.5121\/csit.2019.91713"},{"key":"7045_CR29","doi-asserted-by":"crossref","unstructured":"Howard A, Sandler M, Chu G, Chen LC, Chen B, Tan M, Wang W, Zhu Y, Pang R, Vasudevan V et\u00a0al. (2019) Searching for MobileNetv3. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision pp 1314\u20131324","DOI":"10.1109\/ICCV.2019.00140"},{"key":"7045_CR30","doi-asserted-by":"crossref","unstructured":"Ma N, Zhang X, Zheng H-T, Sun J (2018) ShuffleNet V2: Practical guidelines for efficient CNN architecture design. In: Proceedings of the European Conference on Computer Vision (ECCV) pp 116\u2013131","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"7045_CR31","doi-asserted-by":"crossref","unstructured":"Fang G, Ma X, Song M, Mi MB, Wang X (2023) Depgraph: towards any structural pruning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition pp 16091\u201316101","DOI":"10.1109\/CVPR52729.2023.01544"},{"key":"7045_CR32","doi-asserted-by":"crossref","unstructured":"Shu C, Liu Y, Gao J, Yan Z, Shen C (2021) Channel-wise knowledge distillation for dense prediction. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision pp 5311\u20135320","DOI":"10.1109\/ICCV48922.2021.00526"},{"key":"7045_CR33","doi-asserted-by":"crossref","unstructured":"Yang Z, Li Z, Shao M, Shi D, Yuan Z, Yuan C (2022) Masked generative distillation. In: European Conference on Computer Vision pp 53\u201369. Springer","DOI":"10.1007\/978-3-031-20083-0_4"},{"issue":"3","key":"7045_CR34","doi-asserted-by":"publisher","first-page":"667","DOI":"10.3390\/electronics12030667","volume":"12","author":"M Glu\u010dina","year":"2023","unstructured":"Glu\u010dina M, Anj\u0323eli\u0107 N, Lorencin I, Car Z (2023) Detection and classification of printed circuit boards using YOLO algorithm. Electronics 12(3):667","journal-title":"Electronics"},{"key":"7045_CR35","doi-asserted-by":"crossref","unstructured":"Ling Q, Isa NAM, Asaari MSM (2023) Precise detection for dense PCB components based on modified YOLOv8. IEEE Access","DOI":"10.1109\/ACCESS.2023.3325885"},{"issue":"3","key":"7045_CR36","doi-asserted-by":"publisher","first-page":"297","DOI":"10.54097\/ajst.v7i3.13420","volume":"7","author":"Y Long","year":"2023","unstructured":"Long Y, Li Z, Cai Y, Zhang R, Shen K (2023) PCB defect detection algorithm based on improved YOLOv8. Acad J Sci Technol 7(3):297\u2013304","journal-title":"Acad J Sci Technol"},{"key":"7045_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107628","volume":"129","author":"L Zhang","year":"2024","unstructured":"Zhang L, Chen J, Chen J, Wen Z, Zhou X (2024) LDD-Net: lightweight printed circuit board defect detection network fusing multi-scale features. Eng Appl Artif Intell 129:107628","journal-title":"Eng Appl Artif Intell"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07045-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-07045-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07045-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T07:32:26Z","timestamp":1740382346000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-07045-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,24]]},"references-count":37,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["7045"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-07045-9","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,24]]},"assertion":[{"value":"11 February 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 February 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 that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"An earlier version of this work, titled \"ASTKD-PCB-LDD: A Lightweight High Performance PCB Defect Detection Algorithm,\" was published as a preprint on Research Square (DOI: https:\/\/doi.org\/10.21203\/rs.3.rs-4008736\/v1). This manuscript includes important revisions and additional experimental results that were not part of the preprint.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Preprint declaration"}}],"article-number":"531"}}