{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T10:17:53Z","timestamp":1780481873948,"version":"3.54.1"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T00:00:00Z","timestamp":1780444800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T00:00:00Z","timestamp":1780444800000},"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\/s11227-026-08633-z","type":"journal-article","created":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T09:49:14Z","timestamp":1780480154000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["TLDRT-DETR: adaptive upsampling and dual-activation attention for real-time transmission line defect detection"],"prefix":"10.1007","volume":"82","author":[{"given":"Bing","family":"Su","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifeng","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,3]]},"reference":[{"issue":"8","key":"8633_CR1","doi-asserted-by":"publisher","first-page":"824","DOI":"10.3390\/rs9080824","volume":"9","author":"Y Zhang","year":"2017","unstructured":"Zhang Y, Yuan X, Li W, Chen S (2017) Automatic power line inspection using UAV images. Remote Sens 9(8):824. https:\/\/doi.org\/10.3390\/rs9080824","journal-title":"Remote Sens"},{"issue":"1","key":"8633_CR2","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1007\/s10462-022-10189-2","volume":"56","author":"Y Luo","year":"2023","unstructured":"Luo Y, Yu X, Yang D, Zhou B (2023) A survey of intelligent transmission line inspection based on unmanned aerial vehicle. Artif Intell Rev 56(1):173\u2013201. https:\/\/doi.org\/10.1007\/s10462-022-10189-2","journal-title":"Artif Intell Rev"},{"issue":"5","key":"8633_CR3","doi-asserted-by":"publisher","first-page":"614","DOI":"10.1016\/j.gloei.2023.10.008","volume":"6","author":"D Li","year":"2023","unstructured":"Li D, Wang X, Zhang J, Ji Z (2023) Automated deep learning system for power line inspection image analysis and processing: architecture and design issues. Glob Energy Interconnect 6(5):614\u2013633. https:\/\/doi.org\/10.1016\/j.gloei.2023.10.008","journal-title":"Glob Energy Interconnect"},{"issue":"1","key":"8633_CR4","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1109\/TPWRD.2009.2035427","volume":"25","author":"J Katrasnik","year":"2009","unstructured":"Katrasnik J, Pernus F, Likar B (2009) A survey of mobile robots for distribution power line inspection. IEEE Trans Power Deliv 25(1):485\u2013493. https:\/\/doi.org\/10.1109\/TPWRD.2009.2035427","journal-title":"IEEE Trans Power Deliv"},{"issue":"12","key":"8633_CR5","doi-asserted-by":"publisher","first-page":"9350","DOI":"10.1109\/TIM.2020.3031194","volume":"69","author":"L Yang","year":"2020","unstructured":"Yang L, Fan J, Liu Y, Li E, Peng J, Liang Z (2020) A review on state-of-the-art power line inspection techniques. IEEE Trans Instrum Meas 69(12):9350\u20139365. https:\/\/doi.org\/10.1109\/TIM.2020.3031194","journal-title":"IEEE Trans Instrum Meas"},{"issue":"1","key":"8633_CR6","doi-asserted-by":"publisher","first-page":"149","DOI":"10.3390\/rs16010149","volume":"16","author":"G Tang","year":"2024","unstructured":"Tang G, Ni J, Zhao Y, Gu Y, Cao W (2024) A survey of object detection for UAVs based on deep learning. Remote Sens 16(1):149. https:\/\/doi.org\/10.3390\/rs16010149","journal-title":"Remote Sens"},{"key":"8633_CR7","unstructured":"Vapnik V, Golowich SE, Smola AJ (1996) Support vector method for function approximation, regression estimation and signal processing. Adv Neural Inf Process Syst 9:281\u2013287."},{"key":"8633_CR8","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 (CVPR)","DOI":"10.1109\/CVPR.2014.81"},{"key":"8633_CR9","doi-asserted-by":"publisher","unstructured":"Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV). https:\/\/doi.org\/10.1109\/ICCV.2015.169","DOI":"10.1109\/ICCV.2015.169"},{"issue":"6","key":"8633_CR10","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137\u20131149. https:\/\/doi.org\/10.1109\/TPAMI.2016.2577031","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"8633_CR11","doi-asserted-by":"publisher","unstructured":"Redmon J, Farhadi A (2018) Yolov3: an incremental improvement. https:\/\/doi.org\/10.48550\/arXiv.1804.02767","DOI":"10.48550\/arXiv.1804.02767"},{"key":"8633_CR12","doi-asserted-by":"publisher","unstructured":"Lv W, Xu S, Zhao Y, Wang G, Wei J, Cui C, Du Y, Dang Q, Liu Y (2023) Detrs beat yolos on real-time object detection. https:\/\/doi.org\/10.48550\/arXiv.2304.08069","DOI":"10.48550\/arXiv.2304.08069"},{"key":"8633_CR13","doi-asserted-by":"publisher","unstructured":"Wang J, Chen K, Xu R, Liu Z, Loy CC (2019) Carafe: content-aware reassembly of features. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV). https:\/\/doi.org\/10.1109\/ICCV.2019.00310","DOI":"10.1109\/ICCV.2019.00310"},{"key":"8633_CR14","doi-asserted-by":"publisher","unstructured":"Liu W, Lu H, Fu H, Cao Z (2023) Learning to upsample by learning to sample. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp 6027\u20136037. https:\/\/doi.org\/10.48550\/arXiv.2308.15085","DOI":"10.48550\/arXiv.2308.15085"},{"key":"8633_CR15","unstructured":"Vaswani A, Shazeer N, Parmar N et al (2017) Attention is all you need. In: Advances in neural information processing systems. https:\/\/arxiv.org\/abs\/1706.03762"},{"key":"8633_CR16","doi-asserted-by":"publisher","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) Ssd: single shot multibox detector. In: European Conference on Computer Vision (ECCV). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"8633_CR17","doi-asserted-by":"publisher","unstructured":"Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S (2020) End-to-end object detection with transformers. In: European Conference on Computer Vision (ECCV). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_13","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"8633_CR18","doi-asserted-by":"publisher","unstructured":"Zhu X, Su W, Lu L, Li B, Wang X, Dai J (2021) Deformable detr: deformable transformers for end-to-end object detection. In: International Conference on Learning Representations (ICLR). https:\/\/doi.org\/10.48550\/arXiv.2010.04159","DOI":"10.48550\/arXiv.2010.04159"},{"key":"8633_CR19","doi-asserted-by":"publisher","unstructured":"Meng D, Chen X, Fan Z, Zeng G, Li H, Yuan Y, Sun L, Wang J (2021) Conditional detr for fast training convergence. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV). https:\/\/doi.org\/10.1109\/ICCV48922.2021.00363","DOI":"10.1109\/ICCV48922.2021.00363"},{"key":"8633_CR20","doi-asserted-by":"publisher","unstructured":"Wang Y, Zhang X, Yang T, Sun J (2021) Anchor detr: query design for transformer-based object detection. https:\/\/doi.org\/10.48550\/arXiv.2109.07107","DOI":"10.48550\/arXiv.2109.07107"},{"key":"8633_CR21","doi-asserted-by":"publisher","unstructured":"Li F, Zhang H, Liu S, Guo J, Ni LM, Zhang L (2022) Dn-detr: Accelerate detr training by introducing query denoising. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https:\/\/doi.org\/10.48550\/arXiv.2203.01305","DOI":"10.48550\/arXiv.2203.01305"},{"key":"8633_CR22","doi-asserted-by":"publisher","unstructured":"Liu S, Li F, Zhang H, Yang X, Qi X, Su H, Zhu J, Zhang L (2022) Dab-detr: Dynamic anchor boxes are better queries for detr https:\/\/doi.org\/10.48550\/arXiv.2201.12329","DOI":"10.48550\/arXiv.2201.12329"},{"key":"8633_CR23","doi-asserted-by":"publisher","unstructured":"Zhang H, Li F, Liu S, Zhang L, Su H, Zhu J, Ni LM, Shum HY (2022) Dino: Detr with improved denoising anchor boxes for end-to-end object detection. arXiv e-prints https:\/\/doi.org\/10.48550\/arXiv.2203.03605","DOI":"10.48550\/arXiv.2203.03605"},{"key":"8633_CR24","doi-asserted-by":"publisher","unstructured":"Dai X, Chen Y, Xiao B, Chen D, Liu M, Yuan L, Zhang L (2021) Dynamic head: unifying object detection heads with attentions. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 7373\u20137382. https:\/\/doi.org\/10.1109\/CVPR46437.2021.00729","DOI":"10.1109\/CVPR46437.2021.00729"},{"key":"8633_CR25","doi-asserted-by":"publisher","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 (CVPR). https:\/\/doi.org\/10.1109\/CVPR.2017.106","DOI":"10.1109\/CVPR.2017.106"},{"key":"8633_CR26","doi-asserted-by":"publisher","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 (ICCV). https:\/\/doi.org\/10.1109\/ICCV.2017.89","DOI":"10.1109\/ICCV.2017.89"},{"key":"8633_CR27","doi-asserted-by":"publisher","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 (CVPR). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00165","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"8633_CR28","doi-asserted-by":"publisher","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https:\/\/doi.org\/10.1109\/TPAMI.2019.2913372","DOI":"10.1109\/TPAMI.2019.2913372"},{"key":"8633_CR29","doi-asserted-by":"publisher","unstructured":"Woo S, Park J, Lee J-Y, Kweon IS (2018) Cbam: convolutional block attention module. In: European Conference on Computer Vision (ECCV). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_1","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"8633_CR30","doi-asserted-by":"publisher","unstructured":"Hou Q, Zhou D, Feng J (2021) Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https:\/\/doi.org\/10.1109\/CVPR46437.2021.01350","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"8633_CR31","doi-asserted-by":"publisher","unstructured":"Misra D (2021) Rotate to attend: convolutional triplet attention module. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV). https:\/\/doi.org\/10.48550\/arXiv.2010.03045","DOI":"10.48550\/arXiv.2010.03045"},{"key":"8633_CR32","unstructured":"Yang L, Zhang H, Bai Y, Ding M, Zhang J, Kong T (2021) Simam: a simple, parameter-free attention module for convolutional neural networks. In: Proceedings of the International Conference on Machine Learning (ICML). https:\/\/api.semanticscholar.org\/CorpusID:235825945"},{"issue":"1","key":"8633_CR33","doi-asserted-by":"publisher","first-page":"373","DOI":"10.32604\/cmc.2024.047469","volume":"79","author":"X Lu","year":"2024","unstructured":"Lu X, Jiang C, Ma Z, Li H, Liu Y (2024) A simple and effective surface defect detection method of power line insulators for difficult small objects. Comput Mater Contin 79(1):373. https:\/\/doi.org\/10.32604\/cmc.2024.047469","journal-title":"Comput Mater Contin"},{"key":"8633_CR34","doi-asserted-by":"publisher","first-page":"110730","DOI":"10.1016\/j.engappai.2025.110730","volume":"152","author":"B Duan","year":"2025","unstructured":"Duan B, Wang D, Ma Y, Wang G, Liu H (2025) Multisource data-driven intelligent method for detecting surface defects in cold-rolled copper strips. Eng Appl Artif Intell 152:110730. https:\/\/doi.org\/10.1016\/j.engappai.2025.110730","journal-title":"Eng Appl Artif Intell"},{"key":"8633_CR35","doi-asserted-by":"publisher","first-page":"117410","DOI":"10.1016\/j.measurement.2025.117410","volume":"253","author":"X You","year":"2025","unstructured":"You X, Zhao X (2025) A insulator defect detection network based on improved yolov7 for UAV aerial images. Measurement 253:117410. https:\/\/doi.org\/10.1016\/j.measurement.2025.117410","journal-title":"Measurement"},{"key":"8633_CR36","doi-asserted-by":"publisher","first-page":"1467","DOI":"10.1016\/j.egyr.2024.12.076","volume":"13","author":"Y Zhao","year":"2025","unstructured":"Zhao Y, Zhang G, Luo W, Tang R, Sun Y, Wang P, Liu J, Mei K (2025) Idd-yolov7: a lightweight and efficient feature extraction method for insulator defect detection. Energy Rep 13:1467\u20131487. https:\/\/doi.org\/10.1016\/j.egyr.2024.12.076","journal-title":"Energy Rep"},{"key":"8633_CR37","doi-asserted-by":"publisher","first-page":"129866","DOI":"10.1016\/j.neucom.2025.129866","volume":"634","author":"Y Si","year":"2025","unstructured":"Si Y, Xu H, Zhu X, Zhang W, Dong Y, Chen Y, Li H (2025) Scsa: exploring the synergistic effects between spatial and channel attention. Neurocomputing 634:129866. https:\/\/doi.org\/10.1016\/j.neucom.2025.129866","journal-title":"Neurocomputing"},{"key":"8633_CR38","volume-title":"Microsoft coco: common objects in context","author":"TY Lin","year":"2014","unstructured":"Lin TY, Maire M, Belongie S, Hays J, Zitnick CL (2014) Microsoft coco: common objects in context. Springer International Publishing, Berlin"},{"key":"8633_CR39","unstructured":"Jocher G, Chaurasia A, Qiu J (2024) Ultralytics YOLO. GitHub"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-026-08633-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-026-08633-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-026-08633-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T09:49:21Z","timestamp":1780480161000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-026-08633-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,3]]},"references-count":39,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["8633"],"URL":"https:\/\/doi.org\/10.1007\/s11227-026-08633-z","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,3]]},"assertion":[{"value":"14 January 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 2026","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":"Conflict of interest"}}],"article-number":"473"}}