{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T07:55:50Z","timestamp":1781510150025,"version":"3.54.1"},"reference-count":55,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.neucom.2026.133931","type":"journal-article","created":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T02:26:09Z","timestamp":1778552769000},"page":"133931","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A region-guided super-resolution reconstruction algorithm for player and ball detection"],"prefix":"10.1016","volume":"697","author":[{"given":"Tingyu","family":"Liang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Songyuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renfei","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingqiang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.neucom.2026.133931_bib0005","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1038\/s41597-022-01469-1","article-title":"Scaling up SoccerNet with multi-view spatial localization and re-identification","volume":"9","author":"Cioppa","year":"2022","journal-title":"Sci. Data"},{"issue":"1","key":"10.1016\/j.neucom.2026.133931_bib0010","article-title":"A survey on fault detection for networked systems under communication constraints","volume":"13","author":"Chen","year":"2025","journal-title":"Syst. Sci. Control Eng."},{"key":"10.1016\/j.neucom.2026.133931_bib0015","series-title":"Proceedings of the European Conference on Computer Vision","first-page":"213","article-title":"End-to-end object detection with transformers","author":"Carion","year":"2020"},{"key":"10.1016\/j.neucom.2026.133931_bib0020","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"6154","article-title":"Cascade R-CNN: delving into high quality object detection","author":"Cai","year":"2018"},{"issue":"4","key":"10.1016\/j.neucom.2026.133931_bib0025","article-title":"A novel UAV-based road damage detection algorithm with lightweight convolution and attention mechanism","volume":"4","author":"Chen","year":"2025","journal-title":"Int. J. Netw. Dyn. Intell."},{"key":"10.1016\/j.neucom.2026.133931_bib0030","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"3086","article-title":"Toward real-world single image super-resolution: a new benchmark and a new model","author":"Cai","year":"2019"},{"key":"10.1016\/j.neucom.2026.133931_bib0035","series-title":"Proceedings of the European Conference on Computer Vision","first-page":"391","article-title":"Accelerating the super-resolution convolutional neural network","author":"Dong","year":"2016"},{"key":"10.1016\/j.neucom.2026.133931_bib0040","series-title":"Proceedings of the European Conference on Computer Vision","first-page":"184","article-title":"Learning a deep convolutional network for image super-resolution","author":"Dong","year":"2014"},{"key":"10.1016\/j.neucom.2026.133931_bib0045","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"3490","article-title":"TOOD: task-aligned one-stage object detection","author":"Feng","year":"2021"},{"key":"10.1016\/j.neucom.2026.133931_bib0050","series-title":"Proceedings of the IEEE International Conference on Computer Vision","first-page":"1440","article-title":"Fast R-CNN","author":"Girshick","year":"2015"},{"key":"10.1016\/j.neucom.2026.133931_bib0055","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"580","article-title":"Rich feature hierarchies for accurate object detection and semantic segmentation","author":"Girshick","year":"2014"},{"issue":"4","key":"10.1016\/j.neucom.2026.133931_bib0060","article-title":"A novel CEEMD-based multichannel denoising autoencoder for noise attenuation of surface microseismic data","volume":"4","author":"Guan","year":"2025","journal-title":"Int. J. Netw. Dyn. Intell."},{"key":"10.1016\/j.neucom.2026.133931_bib0065","series-title":"Proceedings of the IEEE International Conference on Computer Vision","first-page":"2961","article-title":"Mask R-CNN","author":"He","year":"2017"},{"key":"10.1016\/j.neucom.2026.133931_bib0070","series-title":"Proceedings of the 22nd Conference on Robots and Vision","article-title":"LeYOLO, new embedded architecture for object detection","author":"Hollard","year":"2025"},{"key":"10.1016\/j.neucom.2026.133931_bib0075","series-title":"International Conference on Neural Information Pocessing","first-page":"387","article-title":"Task-driven super resolution: object detection in low-resolution images","author":"Haris","year":"2021"},{"key":"10.1016\/j.neucom.2026.133931_bib0080","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"2651","article-title":"Beyond image super-resolution for image recognition with task-driven perceptual loss","author":"Kim","year":"2024"},{"key":"10.1016\/j.neucom.2026.133931_bib0085","series-title":"Proceedings of the European Conference on Computer Vision, Lecture Notes in Computer Science","first-page":"231","article-title":"FADE: fusing the assets of decoder and encoder for task-agnostic upsampling","author":"Lu","year":"2022"},{"key":"10.1016\/j.neucom.2026.133931_bib0090","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"6027","article-title":"Learning to upsample by learning to sample","author":"Liu","year":"2023"},{"issue":"4","key":"10.1016\/j.neucom.2026.133931_bib0095","article-title":"Analysis and prospect of wind power forecasting methods from multiple perspectives","volume":"4","author":"Liu","year":"2025","journal-title":"Int. J. Netw. Dyn. Intell."},{"key":"10.1016\/j.neucom.2026.133931_bib0100","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"1132","article-title":"Enhanced deep residual networks for single image super-resolution","author":"Lim","year":"2017"},{"key":"10.1016\/j.neucom.2026.133931_bib0105","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"4681","article-title":"Photo-realistic single image super-resolution using a generative adversarial network","author":"Ledig","year":"2017"},{"issue":"10","key":"10.1016\/j.neucom.2026.133931_bib0110","doi-asserted-by":"crossref","first-page":"12581","DOI":"10.1109\/TPAMI.2023.3282631","article-title":"Uniformer: unifying convolution and self-attention for visual recognition","volume":"45","author":"Li","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"11","key":"10.1016\/j.neucom.2026.133931_bib0115","doi-asserted-by":"crossref","first-page":"2499","DOI":"10.1080\/00207721.2024.2448775","article-title":"Recursive filtering of networked systems with communication protocol scheduling: a survey","volume":"56","author":"Liu","year":"2025","journal-title":"Int. J. Syst. Sci."},{"key":"10.1016\/j.neucom.2026.133931_bib0120","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"13619","article-title":"DN-DETR: accelerate DETR training by introducing query de-noising","author":"Li","year":"2022"},{"key":"10.1016\/j.neucom.2026.133931_bib0125","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"1646","article-title":"Accurate image super-resolution using very deep convolutional networks","author":"Kim","year":"2016"},{"issue":"1","key":"10.1016\/j.neucom.2026.133931_bib0130","doi-asserted-by":"crossref","DOI":"10.1117\/1.JEI.32.1.011003","article-title":"YOLOv3-SORT: detection and tracking player\/ball in soccer sport","volume":"32","author":"Naik","year":"2022","journal-title":"J. Electron. Imaging"},{"issue":"3","key":"10.1016\/j.neucom.2026.133931_bib0135","article-title":"Enhancing visual SLAM localization accuracy through dynamic object detection and adaptive feature filtering","volume":"4","author":"Qiang","year":"2025","journal-title":"Int. J. Netw. Dyn. Intell."},{"key":"10.1016\/j.neucom.2026.133931_bib0140","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"7263","article-title":"YOLO9000: better, faster, stronger","author":"Redmon","year":"2017"},{"key":"10.1016\/j.neucom.2026.133931_bib0145","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"779","article-title":"You only look once: unified, real-time object detection","author":"Redmon","year":"2016"},{"issue":"6","key":"10.1016\/j.neucom.2026.133931_bib0150","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"10.1016\/j.neucom.2026.133931_bib0155","first-page":"1330","article-title":"Badminton player detection using faster region convolutional neural network","volume":"14","author":"Rahmad","year":"2019","journal-title":"Indones. J. Electr. Eng. Comput. Sci."},{"key":"10.1016\/j.neucom.2026.133931_bib0160","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"658","article-title":"Generalized intersection over Union: a metric and a loss for bounding box regression","author":"Rezatofighi","year":"2019"},{"key":"10.1016\/j.neucom.2026.133931_bib0165","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"4510","article-title":"MobileNetV2: inverted residuals and linear bottlenecks","author":"Sandler","year":"2018"},{"key":"10.1016\/j.neucom.2026.133931_bib0170","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"14454","article-title":"Sparse R-CNN: end-to-end object detection with learnable proposals","author":"Sun","year":"2021"},{"issue":"1","key":"10.1016\/j.neucom.2026.133931_bib0175","article-title":"A survey on learning from data with label noise via deep neural networks","volume":"13","author":"Song","year":"2025","journal-title":"Syst. Sci. Control Eng."},{"key":"10.1016\/j.neucom.2026.133931_bib0180","doi-asserted-by":"crossref","DOI":"10.1016\/j.ast.2026.112219","article-title":"Vectorial importance-weighted neural network framework for aviation structural systems multi-failures related reliability estimation","author":"Teng","year":"2026","journal-title":"Aerosp. Sci. Technol."},{"issue":"6","key":"10.1016\/j.neucom.2026.133931_bib0185","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1007\/s42452-025-07116-9","article-title":"Football sports video tracking and detection technology based on YOLOv5 and DeepSORT","volume":"7","author":"Wang","year":"2025","journal-title":"Discov. Appl. Sci."},{"key":"10.1016\/j.neucom.2026.133931_bib0190","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"3007","article-title":"CARAFE: content-aware reassembly of features","author":"Wang","year":"2019"},{"issue":"6","key":"10.1016\/j.neucom.2026.133931_bib0195","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1080\/00207721.2024.2423033","article-title":"Distributed correntropy Kalman filtering over sensor networks with FlexRay-based protocols","volume":"56","author":"Wang","year":"2025","journal-title":"Int. J. Syst. Sci."},{"key":"10.1016\/j.neucom.2026.133931_bib0200","series-title":"Proceedings of the 2020 5th International Conference on Control, Robotics and Cybernetics","first-page":"211","article-title":"Feature-driven super-resolution for object detection","author":"Wang","year":"2020"},{"issue":"8","key":"10.1016\/j.neucom.2026.133931_bib0205","doi-asserted-by":"crossref","first-page":"14479","DOI":"10.1109\/TNNLS.2025.3542719","article-title":"Fusionformer: a novel adversarial transformer utilizing fusion attention for multivariate anomaly detection","volume":"36","author":"Wang","year":"2025","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"2","key":"10.1016\/j.neucom.2026.133931_bib0210","doi-asserted-by":"crossref","first-page":"1374","DOI":"10.1109\/TII.2023.3275701","article-title":"Subdomain-alignment data augmentation for pipeline fault diagnosis: an adversarial self-attention network","volume":"20","author":"Wang","year":"2024","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"1","key":"10.1016\/j.neucom.2026.133931_bib0215","article-title":"AI-driven automation of aviation equipment inspection: insights from a complex adaptive systems perspective","volume":"7","author":"BWWL2026P. Wu","year":"2026","journal-title":"Innovation"},{"key":"10.1016\/j.neucom.2026.133931_bib0220","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops","first-page":"1905","article-title":"Real-ESRGAN: training real-world blind super-resolution with pure synthetic data","author":"Wang","year":"2021"},{"issue":"16","key":"10.1016\/j.neucom.2026.133931_bib0225","doi-asserted-by":"crossref","first-page":"3863","DOI":"10.1080\/00207721.2025.2480201","article-title":"Finite-time control for MJSs under protocol-based fading network and input saturation: a WOA-assisted method","volume":"56","author":"Wang","year":"2025","journal-title":"Int. J. Syst. Sci."},{"key":"10.1016\/j.neucom.2026.133931_bib0230","series-title":"Proceedings of the European Conference on Computer Vision Workshops","article-title":"ESRGAN: enhanced super-resolution generative adversarial networks","author":"Wang","year":"2018"},{"issue":"4","key":"10.1016\/j.neucom.2026.133931_bib0235","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1080\/00207721.2024.2393688","article-title":"Neural-based event-triggered observer design for adaptive sliding mode control of nonlinear networked control systems","volume":"56","author":"Yang","year":"2025","journal-title":"Int. J. Syst. Sci."},{"key":"10.1016\/j.neucom.2026.133931_bib0240","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"9759","article-title":"Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection","author":"Zhang","year":"2020"},{"key":"10.1016\/j.neucom.2026.133931_bib0245","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"4791","article-title":"Designing a practical degradation model for deep blind image super-resolution","author":"Zhang","year":"2021"},{"key":"10.1016\/j.neucom.2026.133931_bib0250","series-title":"Proceedings of the European Conference on Computer Vision","first-page":"286","article-title":"Image super-resolution using very deep residual channel attention networks","author":"Zhang","year":"2018"},{"key":"10.1016\/j.neucom.2026.133931_bib0255","series-title":"Pacific Rim International Conference on Artificial Intelligence","first-page":"438","article-title":"RC-CNN: reverse connected convolutional neural network for accurate player detection","author":"Zhang","year":"2018"},{"key":"10.1016\/j.neucom.2026.133931_bib0260","series-title":"Proceedings of the International Conference on Learning Representations","article-title":"Deformable transformers for end-to-end object detection","author":"Zhu","year":"2021"},{"key":"10.1016\/j.neucom.2026.133931_bib0265","first-page":"1","article-title":"A novel fusion attention-based lightweight model for pipeline weld multiscale defect detection","author":"Zhang","year":"2026","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"1","key":"10.1016\/j.neucom.2026.133931_bib0270","article-title":"A systematic literature review on incomplete multimodal learning: techniques and challenges","volume":"13","author":"Zhan","year":"2025","journal-title":"Syst. Sci. Control Eng."},{"issue":"1","key":"10.1016\/j.neucom.2026.133931_bib0275","article-title":"Real-time semantic segmentation of road scenes via hybrid dilated grouping network","volume":"4","author":"Zhang","year":"2025","journal-title":"Int. J. Netw. Dyn. Intell."}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226013287?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226013287?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T07:46:18Z","timestamp":1781509578000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231226013287"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":55,"alternative-id":["S0925231226013287"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133931","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A region-guided super-resolution reconstruction algorithm for player and ball detection","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133931","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"133931"}}