{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,14]],"date-time":"2026-11-14T03:34:44Z","timestamp":1794627284423,"version":"4.9.1"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T00:00:00Z","timestamp":1765411200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T00:00:00Z","timestamp":1768435200000},"content-version":"vor","delay-in-days":35,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100003665","name":"National IT Industry Promotion Agency","doi-asserted-by":"publisher","award":["(IITP-2025-RS-2020-II201462, 50%)"],"award-info":[{"award-number":["(IITP-2025-RS-2020-II201462, 50%)"]}],"id":[{"id":"10.13039\/501100003665","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003665","name":"National IT Industry Promotion Agency","doi-asserted-by":"publisher","award":["(IITP-2025-RS-2020-II201462, 50%)"],"award-info":[{"award-number":["(IITP-2025-RS-2020-II201462, 50%)"]}],"id":[{"id":"10.13039\/501100003665","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003665","name":"National IT Industry Promotion Agency","doi-asserted-by":"publisher","award":["(IITP-2025-RS-2020-II201462, 50%)"],"award-info":[{"award-number":["(IITP-2025-RS-2020-II201462, 50%)"]}],"id":[{"id":"10.13039\/501100003665","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003665","name":"National IT Industry Promotion Agency","doi-asserted-by":"publisher","award":["(IITP-2025-RS-2020-II201462, 50%)"],"award-info":[{"award-number":["(IITP-2025-RS-2020-II201462, 50%)"]}],"id":[{"id":"10.13039\/501100003665","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003665","name":"National IT Industry Promotion Agency","doi-asserted-by":"publisher","award":["(IITP-2025-RS-2020-II201462, 50%)"],"award-info":[{"award-number":["(IITP-2025-RS-2020-II201462, 50%)"]}],"id":[{"id":"10.13039\/501100003665","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-31654-2","type":"journal-article","created":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T09:47:03Z","timestamp":1765446423000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Attention-guided hybrid learning for accurate defect classification in manufacturing environments"],"prefix":"10.1038","volume":"16","author":[{"given":"Mahmoud SalahEldin","family":"Kasem","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed","family":"Mahmoud","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mostafa Farouk","family":"Senussi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahmoud","family":"Abdalla","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyun-Soo","family":"Kang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,11]]},"reference":[{"issue":"9","key":"31654_CR1","doi-asserted-by":"publisher","first-page":"10483","DOI":"10.1109\/TITS.2024.3365296","volume":"25","author":"D Amirkhani","year":"2024","unstructured":"Amirkhani, D., Allili, M. S., Hebbache, L., Hammouche, N. & Lapointe, J.-F. Visual concrete bridge defect classification and detection using deep learning: A systematic review. IEEE Trans Intell Transp Syst 25(9), 10483\u201310505 (2024).","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"31654_CR2","doi-asserted-by":"crossref","unstructured":"Khanam, R., Hussain, M., Hill, R. & Allen, P. A comprehensive review of convolutional neural networks for defect detection in industrial applications. IEEE Access (2024).","DOI":"10.1109\/ACCESS.2024.3425166"},{"key":"31654_CR3","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1186\/s40537-023-00708-5","volume":"10","author":"EA Elaziz","year":"2023","unstructured":"Elaziz, E. A., Fathalla, R. & Shaheen, M. Deep reinforcement learning for data-efficient weakly supervised business process anomaly detection. J Big Data 10, 33 (2023).","journal-title":"J Big Data"},{"key":"31654_CR4","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.3390\/math13081226","volume":"13","author":"M Mahmoud","year":"2025","unstructured":"Mahmoud, M. et al. Two-stage video violence detection framework using gmflow and cbam-enhanced resnet3d. Mathematics 13, 1226 (2025).","journal-title":"Mathematics"},{"key":"31654_CR5","doi-asserted-by":"publisher","first-page":"266","DOI":"10.3390\/math13020266","volume":"13","author":"MS Kasem","year":"2025","unstructured":"Kasem, M. S. et al. HTTD: A hierarchical transformer for accurate table detection in document images. Mathematics 13, 266 (2025).","journal-title":"Mathematics"},{"key":"31654_CR6","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1186\/s40537-024-00940-7","volume":"11","author":"W Sun","year":"2024","unstructured":"Sun, W. et al. On data efficiency of univariate time series anomaly detection models. J Big Data 11, 83 (2024).","journal-title":"J Big Data"},{"key":"31654_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2023.103911","volume":"148","author":"SB Jha","year":"2023","unstructured":"Jha, S. B. & Babiceanu, R. F. Deep CNN-based visual defect detection: Survey of current literature. Comput Ind 148, 103911 (2023).","journal-title":"Comput Ind"},{"key":"31654_CR8","doi-asserted-by":"publisher","first-page":"10651","DOI":"10.1007\/s10462-023-10438-y","volume":"56","author":"D Zhang","year":"2023","unstructured":"Zhang, D. et al. An efficient lightweight convolutional neural network for industrial surface defect detection. Artif Intell Rev 56, 10651\u201310677 (2023).","journal-title":"Artif Intell Rev"},{"key":"31654_CR9","doi-asserted-by":"crossref","unstructured":"Rasheed, M.\u00a0R. et al. An efficientnet-based transfer learning system for defect classification in manufacturing. In 2024 IEEE 22nd International Conference on Industrial Informatics (INDIN), 1\u20137 (IEEE, 2024).","DOI":"10.1109\/INDIN58382.2024.10774443"},{"key":"31654_CR10","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1186\/s40537-025-01065-1","volume":"12","author":"ZF Elsharkawy","year":"2025","unstructured":"Elsharkawy, Z. F., Kasban, H. & Abbass, M. Y. Efficient surface crack segmentation for industrial and civil applications based on an enhanced yolov8 model. J Big Data 12, 16 (2025).","journal-title":"J Big Data"},{"key":"31654_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2020.103514","volume":"122","author":"Z Yu","year":"2021","unstructured":"Yu, Z., Shen, Y. & Shen, C. A real-time detection approach for bridge cracks based on yolov4-fpm. Autom Construct 122, 103514 (2021).","journal-title":"Autom Construct"},{"key":"31654_CR12","doi-asserted-by":"publisher","first-page":"9756","DOI":"10.1038\/s41598-025-94109-8","volume":"15","author":"J-H Tian","year":"2025","unstructured":"Tian, J.-H. et al. An improved yolov5n algorithm for detecting surface defects in industrial components. Sci Rep 15, 9756 (2025).","journal-title":"Sci Rep"},{"key":"31654_CR13","doi-asserted-by":"publisher","first-page":"1104","DOI":"10.3390\/electronics14061104","volume":"14","author":"S Kang","year":"2025","unstructured":"Kang, S., Hu, Z., Liu, L., Zhang, K. & Cao, Z. Object detection yolo algorithms and their industrial applications: Overview and comparative analysis. Electronics 14, 1104 (2025).","journal-title":"Electronics"},{"key":"31654_CR14","doi-asserted-by":"crossref","unstructured":"Nikam, D., Chukwuemeke, A., Nigam, A., Bhosale, T. & Nikam, S. On the application of yolo-based object detection models to classify and detect defects in laser-directed energy deposition process. Progress in Additive Manufacturing 1\u201316 (2025).","DOI":"10.1007\/s40964-025-01056-x"},{"key":"31654_CR15","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y. & Kweon, I.\u00a0S. Cbam: Convolutional block attention module. In Proceedings of the European Conference on Computer Vision (ECCV), 3\u201319 (2018).","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"31654_CR16","doi-asserted-by":"crossref","unstructured":"Sun, Z. et al. Alpha-clip: A clip model focusing on wherever you want. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 13019\u201313029 (2024).","DOI":"10.1109\/CVPR52733.2024.01237"},{"key":"31654_CR17","doi-asserted-by":"crossref","unstructured":"Zhong, Y. et al. Regionclip: Region-based language-image pretraining. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 16793\u201316803 (2022).","DOI":"10.1109\/CVPR52688.2022.01629"},{"key":"31654_CR18","doi-asserted-by":"crossref","unstructured":"Lyu, S. et al. Mvrec: A general few-shot defect classification model using multi-view region-context. In Proceedings of the AAAI Conference on Artificial Intelligence vol. 39, 5937\u20135945 (2025).","DOI":"10.1609\/aaai.v39i6.32634"},{"key":"31654_CR19","doi-asserted-by":"publisher","first-page":"2337","DOI":"10.1007\/s11263-022-01653-1","volume":"130","author":"K Zhou","year":"2022","unstructured":"Zhou, K., Yang, J., Loy, C. C. & Liu, Z. Learning to prompt for vision-language models. Int J Comput Vision 130, 2337\u20132348 (2022).","journal-title":"Int J Comput Vision"},{"key":"31654_CR20","doi-asserted-by":"crossref","unstructured":"Udandarao, V., Gupta, A. & Albanie, S. Sus-x: Training-free name-only transfer of vision-language models. In Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2725\u20132736 (2023).","DOI":"10.1109\/ICCV51070.2023.00257"},{"key":"31654_CR21","unstructured":"Ultralytics YOLO11: Models and image classification task. https:\/\/docs.ultralytics.com\/models\/yolo11\/ (2024)."},{"key":"31654_CR22","unstructured":"Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning, 6105\u20136114 (PMLR, 2019)."},{"key":"31654_CR23","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y. et al. Feature pyramid networks for object detection. In CVPR (2017).","DOI":"10.1109\/CVPR.2017.106"},{"key":"31654_CR24","doi-asserted-by":"publisher","first-page":"858","DOI":"10.1016\/j.apsusc.2013.09.002","volume":"285","author":"K Song","year":"2013","unstructured":"Song, K. & Yan, Y. A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects. Appl Surf Sci 285, 858\u2013864 (2013).","journal-title":"Appl Surf Sci"},{"key":"31654_CR25","unstructured":"Wieler, M. & Hahn, T. Weakly supervised learning for industrial optical inspection. In: DAGM Symposium In 6, 11 (2007)."},{"key":"31654_CR26","doi-asserted-by":"publisher","first-page":"759","DOI":"10.1007\/s10845-019-01476-x","volume":"31","author":"D Tabernik","year":"2020","unstructured":"Tabernik, D., \u0160ela, S., Skvar\u010d, J. & Sko\u010daj, D. Segmentation-based deep-learning approach for surface-defect detection. J Intell Manuf 31, 759\u2013776 (2020).","journal-title":"J Intell Manuf"},{"key":"31654_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2021.103459","volume":"129","author":"J Bo\u017ei\u010d","year":"2021","unstructured":"Bo\u017ei\u010d, J., Tabernik, D. & Sko\u010daj, D. Mixed supervision for surface-defect detection: From weakly to fully supervised learning. Comput Ind 129, 103459 (2021).","journal-title":"Comput Ind"},{"key":"31654_CR28","unstructured":"Kaggle & Severstal. Severstal: Steel defect detection. https:\/\/www.kaggle.com\/competitions\/severstal-steel-defect-detection (2019). Kaggle Competition."},{"key":"31654_CR29","unstructured":"Tang, S., He, F., Huang, X. & Yang, J. Online PCB defect detector on a new PCB defect dataset. arXiv preprint arXiv:1902.06197 (2019)."},{"key":"31654_CR30","doi-asserted-by":"crossref","unstructured":"Bergmann, P., Fauser, M., Sattlegger, D. & Steger, C. Mvtec ad\u2013a comprehensive real-world dataset for unsupervised anomaly detection. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 9592\u20139600 (2019).","DOI":"10.1109\/CVPR.2019.00982"},{"key":"31654_CR31","doi-asserted-by":"publisher","first-page":"3119","DOI":"10.1007\/s11760-023-02533-y","volume":"17","author":"X Zou","year":"2023","unstructured":"Zou, X., Wu, C., Liu, H. & Yu, Z. Improved resnet-50 model for identifying defects on wood surfaces. Signal, Image Video Process 17, 3119\u20133126 (2023).","journal-title":"Signal, Image Video Process"},{"key":"31654_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115673","volume":"185","author":"H Zhang","year":"2021","unstructured":"Zhang, H., Jiang, L. & Li, C. Cs-resnet: Cost-sensitive residual convolutional neural network for PCB cosmetic defect detection. Expert Syst Appl 185, 115673 (2021).","journal-title":"Expert Syst Appl"},{"key":"31654_CR33","doi-asserted-by":"crossref","unstructured":"Anand, P., Sudharsan, R.\u00a0M., Ramachandran, V. & Prusty, M.\u00a0R. Steel defect detection using a resnet-inspired model and fourier transforms. In 2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), 1\u20136 (IEEE, 2024).","DOI":"10.1109\/ICAECT60202.2024.10469579"},{"key":"31654_CR34","doi-asserted-by":"publisher","first-page":"79553","DOI":"10.1109\/ACCESS.2022.3193676","volume":"10","author":"T Yu","year":"2022","unstructured":"Yu, T., Chen, W., Junfeng, G. & Poxi, H. Intelligent detection method of forgings defects detection based on improved efficientnet and memetic algorithm. IEEE Access 10, 79553\u201379563 (2022).","journal-title":"IEEE Access"},{"key":"31654_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2024.102737","volume":"62","author":"Y Zhang","year":"2024","unstructured":"Zhang, Y., Zhan, Q. & Ma, Z. Efficientnet-eca: A lightweight network based on efficient channel attention for class-imbalanced welding defects classification. Adv Eng Inf 62, 102737 (2024).","journal-title":"Adv Eng Inf"},{"issue":"11","key":"31654_CR36","doi-asserted-by":"publisher","first-page":"8995","DOI":"10.1007\/s11042-024-18853-1","volume":"84","author":"K Sooda","year":"2024","unstructured":"Sooda, K. & Rai, K. B. Efficientnet-b7 framework for anomaly detection in mammogram images. Multimed Tools Appl 84(11), 8995\u20139021 (2024).","journal-title":"Multimed Tools Appl"},{"key":"31654_CR37","doi-asserted-by":"crossref","unstructured":"Wu, D. et al. Efficientnet-b0-based 3d quantification algorithm for rectangular defects in pipelines. IEEE Access (2025).","DOI":"10.1109\/ACCESS.2025.3525540"},{"key":"31654_CR38","doi-asserted-by":"publisher","first-page":"25066","DOI":"10.1109\/ACCESS.2024.3359639","volume":"12","author":"TS Chi","year":"2024","unstructured":"Chi, T. S. et al. Enhancing efficientnet-yolov4 for integrated circuit detection on printed circuit board (PCB). IEEE Access 12, 25066\u201325078 (2024).","journal-title":"IEEE Access"},{"key":"31654_CR39","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R. & Farhadi, A. You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 779\u2013788 (2016).","DOI":"10.1109\/CVPR.2016.91"},{"key":"31654_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11554-024-01585-8","volume":"22","author":"J Zhong","year":"2025","unstructured":"Zhong, J., Qian, H., Wang, H., Wang, W. & Zhou, Y. Improved real-time object detection method based on yolov8: A refined approach. J Real-Time Image Process 22, 1\u201313 (2025).","journal-title":"J Real-Time Image Process"},{"key":"31654_CR41","first-page":"1","volume":"73","author":"M Yuan","year":"2024","unstructured":"Yuan, M. et al. Yolo-hmc: An improved method for PCB surface defect detection. IEEE Trans Instrum Meas 73, 1\u201311 (2024).","journal-title":"IEEE Trans Instrum Meas"},{"key":"31654_CR42","doi-asserted-by":"publisher","first-page":"28946","DOI":"10.1038\/s41598-024-78598-7","volume":"14","author":"HL Gope","year":"2024","unstructured":"Gope, H. L., Fukai, H., Ruhad, F. M. & Barman, S. Comparative analysis of yolo models for green coffee bean detection and defect classification. Sci Rep 14, 28946 (2024).","journal-title":"Sci Rep"},{"key":"31654_CR43","doi-asserted-by":"publisher","first-page":"7351","DOI":"10.1038\/s41598-024-57491-3","volume":"14","author":"G Xiao","year":"2024","unstructured":"Xiao, G., Hou, S. & Zhou, H. PCB defect detection algorithm based on cdi-yolo. Sci Rep 14, 7351 (2024).","journal-title":"Sci Rep"},{"key":"31654_CR44","first-page":"1","volume":"73","author":"W Zhou","year":"2024","unstructured":"Zhou, W. et al. An efficient tiny defect detection method for PCB with improved yolo through a compression training strategy. IEEE Trans Instrum Meas 73, 1\u201314 (2024).","journal-title":"IEEE Trans Instrum Meas"},{"key":"31654_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2024.115060","volume":"236","author":"M Lu","year":"2024","unstructured":"Lu, M., Sheng, W., Zou, Y., Chen, Y. & Chen, Z. Wss-yolo: An improved industrial defect detection network for steel surface defects. Measurement 236, 115060 (2024).","journal-title":"Measurement"},{"key":"31654_CR46","doi-asserted-by":"publisher","first-page":"831","DOI":"10.3390\/math13050831","volume":"13","author":"G Zhu","year":"2025","unstructured":"Zhu, G., Qi, H. & Lv, K. Dgyolov8: An enhanced model for steel surface defect detection based on yolov8. Mathematics 13, 831 (2025).","journal-title":"Mathematics"},{"key":"31654_CR47","first-page":"27730","volume":"35","author":"L Ouyang","year":"2022","unstructured":"Ouyang, L. et al. Training language models to follow instructions with human feedback. Adv Neural Inf Process Syst 35, 27730\u201327744 (2022).","journal-title":"Adv Neural Inf Process Syst"},{"key":"31654_CR48","unstructured":"Zhu, D., Chen, J., Shen, X., Li, X. & Elhoseiny, M. Minigpt-4: Enhancing vision-language understanding with advanced large language models. arXiv preprint arXiv:2304.10592 (2023)."},{"key":"31654_CR49","unstructured":"Radford, A. et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, 8748\u20138763 (PmLR, 2021)."},{"key":"31654_CR50","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1007\/s11263-023-01891-x","volume":"132","author":"P Gao","year":"2024","unstructured":"Gao, P. et al. Clip-adapter: Better vision-language models with feature adapters. Int J Comput Vision 132, 581\u2013595 (2024).","journal-title":"Int J Comput Vision"},{"key":"31654_CR51","doi-asserted-by":"crossref","unstructured":"Zhang, R. et al. Tip-adapter: Training-free adaption of clip for few-shot classification. In European conference on computer vision, 493\u2013510 (Springer, 2022).","DOI":"10.1007\/978-3-031-19833-5_29"},{"key":"31654_CR52","doi-asserted-by":"crossref","unstructured":"Shtedritski, A., Rupprecht, C. & Vedaldi, A. What does clip know about a red circle? visual prompt engineering for vlms. In Proceedings of the IEEE\/CVF International Conference on Computer Vision, 11987\u201311997 (2023).","DOI":"10.1109\/ICCV51070.2023.01101"},{"key":"31654_CR53","doi-asserted-by":"publisher","first-page":"731","DOI":"10.1111\/mice.12334","volume":"33","author":"Y-J Cha","year":"2018","unstructured":"Cha, Y.-J., Choi, W., Suh, G., Mahmoudkhani, S. & B\u00fcy\u00fck\u00f6zt\u00fcrk, O. Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types. Comput-Aided Civil Infrastruct Eng 33, 731\u2013747 (2018).","journal-title":"Comput-Aided Civil Infrastruct Eng"},{"key":"31654_CR54","doi-asserted-by":"publisher","first-page":"36","DOI":"10.47852\/bonviewMEDIN42023518","volume":"2","author":"I Dheivya","year":"2025","unstructured":"Dheivya, I. & Kumar, G. S. Vsegnet-a variant segnet for improving segmentation accuracy in medical images with class imbalance and limited data. Medinformatics 2, 36\u201348 (2025).","journal-title":"Medinformatics"},{"key":"31654_CR55","doi-asserted-by":"publisher","first-page":"176","DOI":"10.47852\/bonviewMEDIN42023535","volume":"1","author":"GA Gellert","year":"2024","unstructured":"Gellert, G. A. et al. Virtual triage early detection of inappropriate care-seeking intent among patients with life-threatening cardiac symptoms. Medinformatics 1, 176\u2013183 (2024).","journal-title":"Medinformatics"},{"key":"31654_CR56","doi-asserted-by":"publisher","first-page":"1493","DOI":"10.1109\/TIM.2019.2915404","volume":"69","author":"Y He","year":"2019","unstructured":"He, Y., Song, K., Meng, Q. & Yan, Y. An end-to-end steel surface defect detection approach via fusing multiple hierarchical features. IEEE Trans Instrum Meas 69, 1493\u20131504 (2019).","journal-title":"IEEE Trans Instrum Meas"},{"key":"31654_CR57","unstructured":"Huang, W. & Wei, P. A pcb dataset for defects detection and classification. arXiv preprint arXiv:1901.08204 (2019)."},{"key":"31654_CR58","doi-asserted-by":"crossref","unstructured":"Wang, Y. & Zell, A. Yolo+ fpn: 2d and 3d fused object detection with an rgb-d camera. In 2020 25th International Conference on Pattern Recognition (ICPR), 4657\u20134664 (IEEE, 2021).","DOI":"10.1109\/ICPR48806.2021.9413066"},{"key":"31654_CR59","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1049\/ipr2.12340","volume":"16","author":"G Wang","year":"2022","unstructured":"Wang, G., Ding, H., Li, B., Nie, R. & Zhao, Y. Trident-yolo: Improving the precision and speed of mobile device object detection. IET Image Process 16, 145\u2013157 (2022).","journal-title":"IET Image Process"},{"key":"31654_CR60","doi-asserted-by":"crossref","unstructured":"Yang, X., Mohamed, A. S.\u00a0A. & Wang, C. Shadowfpn-yolo: A real-time nms-free detector for remote sensing ship detection. IEEE Access (2025).","DOI":"10.1109\/ACCESS.2025.3554018"},{"key":"31654_CR61","doi-asserted-by":"publisher","first-page":"36978","DOI":"10.1038\/s41598-025-20786-0","volume":"15","author":"MF Senussi","year":"2025","unstructured":"Senussi, M. F., Abdalla, M., Kasem, M. S., Mahmoud, M. & Kang, H.-S. Learning to remove occlusions in light field images using multiscale receptive fields and feature pyramid networks. Sci Rep 15, 36978 (2025).","journal-title":"Sci Rep"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-31654-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-31654-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-31654-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T01:03:29Z","timestamp":1768525409000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-31654-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,11]]},"references-count":61,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["31654"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-31654-2","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,11]]},"assertion":[{"value":"9 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 December 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"}}],"article-number":"2018"}}