{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T04:48:07Z","timestamp":1778906887681,"version":"3.51.4"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,2,17]],"date-time":"2025-02-17T00:00:00Z","timestamp":1739750400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,17]],"date-time":"2025-02-17T00:00:00Z","timestamp":1739750400000},"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\/s11227-025-07031-1","type":"journal-article","created":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T01:40:21Z","timestamp":1739842821000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["MOKP-YOLO: a unified high-performance model for military object and key part detection in UAV images"],"prefix":"10.1007","volume":"81","author":[{"given":"Keshun","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaotong","family":"Zuo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaolin","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changlong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sen","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,17]]},"reference":[{"key":"7031_CR1","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 16:149","journal-title":"Remote Sens"},{"key":"7031_CR2","doi-asserted-by":"publisher","first-page":"1288","DOI":"10.3390\/rs16071288","volume":"16","author":"B Zeng","year":"2024","unstructured":"Zeng B, Gao S, Xu Y, Zhang Z, Li F, Wang C (2024) Detection of military targets on ground and sea by uavs with low-altitude oblique perspective. Remote Sens 16:1288","journal-title":"Remote Sens"},{"key":"7031_CR3","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1007\/s11554-024-01444-6","volume":"21","author":"H Zhu","year":"2024","unstructured":"Zhu H, Huang Y, Xu Y, Zhou J, Deng F, Zhai Y (2024) Unmanned aerial vehicle (uav) object detection algorithm based on keypoints representation and rotated distance-iou loss. J Real-Time Image Process 21:58","journal-title":"J Real-Time Image Process"},{"issue":"11","key":"7031_CR4","doi-asserted-by":"publisher","first-page":"4758","DOI":"10.3390\/app11114758","volume":"11","author":"A Malta","year":"2021","unstructured":"Malta A, Mendes M, Farinha T (2021) Augmented reality maintenance assistant using yolov5. Appl Sci 11(11):4758","journal-title":"Appl Sci"},{"key":"7031_CR5","first-page":"5008213","volume":"71","author":"H Zhang","year":"2022","unstructured":"Zhang H, Wu L, Chen Y, Chen R, Kong S, Wang Y, Hu J, Wu J (2022) Attention-guided multitask convolutional neural network for power line parts detection. IEEE Trans Instrum Measur 71:5008213","journal-title":"IEEE Trans Instrum Measur"},{"key":"7031_CR6","doi-asserted-by":"publisher","first-page":"8737","DOI":"10.1109\/JSTARS.2024.3389072","volume":"17","author":"H Wang","year":"2024","unstructured":"Wang H, Shen Q, Li J, Chen Z, Guo Y, Zhang S (2024) A classwise vulnerable part detection method for military targets. IEEE J Select Top Appl Earth Observ Remote Sens 17:8737\u20138750","journal-title":"IEEE J Select Top Appl Earth Observ Remote Sens"},{"key":"7031_CR7","first-page":"5033515","volume":"73","author":"H Wang","year":"2024","unstructured":"Wang H, Shen Q, Deng Z, Guo Y, Zhang S (2024) A joint detection method for military targets and their key parts for uav images. IEEE Trans Instrum Measur 73:5033515","journal-title":"IEEE Trans Instrum Measur"},{"key":"7031_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.128748","volume":"612","author":"H Wang","year":"2025","unstructured":"Wang H, Shen Q, Deng Z (2025) A diverse knowledge perception and fusion network for detecting targets and key parts in uav images. Neurocomputing 612:128748","journal-title":"Neurocomputing"},{"issue":"11","key":"7031_CR9","doi-asserted-by":"publisher","first-page":"736","DOI":"10.3390\/ijgi10110736","volume":"10","author":"H Fu","year":"2021","unstructured":"Fu H, Fan X, Yan Z, Du X (2021) Detection of schools in remote sensing images based on attention-guided dense network. ISPRS Int J Geo-inform 10(11):736","journal-title":"ISPRS Int J Geo-inform"},{"key":"7031_CR10","doi-asserted-by":"publisher","first-page":"2826","DOI":"10.1109\/TIP.2021.3055617","volume":"30","author":"Y Ding","year":"2021","unstructured":"Ding Y, Ma Z, Wen S, Xie J, Chang D, Si Z, Wu M, Ling H (2021) Ap-cnn: weakly supervised attention pyramid convolutional neural network for fine-grained visual classification. IEEE Trans Image Process 30:2826\u20132836","journal-title":"IEEE Trans Image Process"},{"key":"7031_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.127254","volume":"573","author":"Y Xu","year":"2024","unstructured":"Xu Y, Zhang Y, Leng Y, Gao Q (2024) Aip-net: an anchor-free instance-level human part detection network. Neurocomputing 573:127254","journal-title":"Neurocomputing"},{"issue":"6","key":"7031_CR12","doi-asserted-by":"publisher","first-page":"2180","DOI":"10.3390\/s21062180","volume":"21","author":"C Liu","year":"2021","unstructured":"Liu C, Sziranyi T (2021) Real-time human detection and gesture recognition for on-board uav rescue. Sensors 21(6):2180","journal-title":"Sensors"},{"issue":"8","key":"7031_CR13","doi-asserted-by":"publisher","first-page":"2650","DOI":"10.3390\/s21082650","volume":"21","author":"D Choi","year":"2021","unstructured":"Choi D, Bell W, Kim D, Kim J (2021) Uav-driven structural crack detection and location determination using convolutional neural networks. Sensors 21(8):2650","journal-title":"Sensors"},{"key":"7031_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2023.105014","volume":"154","author":"X He","year":"2023","unstructured":"He X, Tang Z, Deng Y, Zhou G, Wang Y, Li L (2023) Uav-based road crack object-detection algorithm. Autom Construct 154:105014","journal-title":"Autom Construct"},{"key":"7031_CR15","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.isprsjprs.2020.12.015","volume":"173","author":"X Sun","year":"2021","unstructured":"Sun X, Wang P, Wang C, Liu Y, Fu K (2021) Pbnet: part-based convolutional neural network for complex composite object detection in remote sensing imagery. ISPRS J Photogram Remote Sens 173:50\u201365","journal-title":"ISPRS J Photogram Remote Sens"},{"key":"7031_CR16","first-page":"1","volume":"71","author":"N Zeng","year":"2022","unstructured":"Zeng N, Wu P, Wang Z, Li H, Liu W, Liu X (2022) A small-sized object detection oriented multi-scale feature fusion approach with application to defect detection. IEEE Trans Instrum Measur 71:1\u201314","journal-title":"IEEE Trans Instrum Measur"},{"issue":"9","key":"7031_CR17","doi-asserted-by":"publisher","first-page":"4934","DOI":"10.1109\/TCSVT.2023.3245883","volume":"33","author":"Y Liu","year":"2023","unstructured":"Liu Y, Li H, Cheng J, Chen X (2023) Mscaf-net: a general framework for camouflaged object detection via learning multi-scale context-aware features. IEEE Trans Circuits Syst Video Technol 33(9):4934\u20134947","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"7031_CR18","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/j.neucom.2020.12.093","volume":"433","author":"J Leng","year":"2021","unstructured":"Leng J, Ren Y, Jiang W, Sun X, Wang Y (2021) Realize your surroundings: exploiting context information for small object detection. Neurocomputing 433:287\u2013299","journal-title":"Neurocomputing"},{"issue":"4","key":"7031_CR19","doi-asserted-by":"publisher","first-page":"662","DOI":"10.1109\/LGRS.2020.2981255","volume":"18","author":"Y Zhao","year":"2021","unstructured":"Zhao Y, Zhao L, Li C (2021) Pyramid attention dilated network for aircraft detection in sar images. IEEE Geosci Remote Sens Lett 18(4):662\u2013666","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"9","key":"7031_CR20","doi-asserted-by":"publisher","first-page":"3456","DOI":"10.1109\/TCSVT.2020.3038649","volume":"31","author":"J Nie","year":"2021","unstructured":"Nie J, Pang Y, Zhao S, Han J, Li X (2021) Efficient selective context network for accurate object detection. IEEE Trans Circuits Syst Video Technol 31(9):3456\u20133468","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"7031_CR21","unstructured":"Redmon J, Farhadi A (2018) YOLOv3: an incremental improvement. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2767\u20132772"},{"key":"7031_CR22","doi-asserted-by":"publisher","first-page":"857","DOI":"10.3390\/rs16050857","volume":"16","author":"J Wang","year":"2024","unstructured":"Wang J, Bai Z, Zhang X, Qiu Y (2024) A lightweight remote sensing aircraft object detection network based on improved yolov5n. Remote Sens 16:857","journal-title":"Remote Sens"},{"key":"7031_CR23","doi-asserted-by":"publisher","first-page":"11463","DOI":"10.3390\/su151411463","volume":"15","author":"D Zhong","year":"2023","unstructured":"Zhong D, Li T, Pan Z, Guo J (2023) Aircraft target detection from remote sensing images under complex meteorological conditions. Sustainability 15:11463","journal-title":"Sustainability"},{"key":"7031_CR24","doi-asserted-by":"publisher","first-page":"17753","DOI":"10.1109\/JSTARS.2024.3462745","volume":"17","author":"X Chen","year":"2024","unstructured":"Chen X, Jiang H, Zheng H, Yang J, Liang R, Xiang D, Cheng H, Jiang Z (2024) Det-yolo: an innovative high-performance model for detecting military aircraft in remote sensing images. IEEE J Select Topics Appl Earth Observ Remote Sens 17:17753\u201317771","journal-title":"IEEE J Select Topics Appl Earth Observ Remote Sens"},{"key":"7031_CR25","doi-asserted-by":"publisher","first-page":"276","DOI":"10.3390\/drones8070276","volume":"8","author":"M Yue","year":"2024","unstructured":"Yue M, Zhang L, Huang J, Zhang H (2024) Lightweight and efficient tiny-object detection based on improved yolov8n for uav aerial images. Drones 8:276","journal-title":"Drones"},{"key":"7031_CR26","doi-asserted-by":"publisher","first-page":"2590","DOI":"10.3390\/rs16142590","volume":"16","author":"J Zhang","year":"2024","unstructured":"Zhang J, Zhang Y, Shi Z, Zhang Y, Gao R (2024) Unmanned aerial vehicle object detection based on information-preserving and fine-grained feature aggregation. Remote Sens 16:2590","journal-title":"Remote Sens"},{"key":"7031_CR27","unstructured":"Pan Z, Cai J, Zhuang B (2022) Fast vision transformers with hilo attention. In: Proceedings of the Conference on Neural Information Processing Systems, pp 14541\u201314554"},{"key":"7031_CR28","doi-asserted-by":"crossref","unstructured":"Qi Y, He Y, Qi X, Zhang Y, Yang G (2023) Dynamic snake convolution based on topological geometric constraints for tubular structure segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp 6070\u20136079","DOI":"10.1109\/ICCV51070.2023.00558"},{"key":"7031_CR29","unstructured":"Yu F, Koltun V (2015) Multi-scale context aggregation by dilated convolutions. In: Proceedings of International Conference on Learning Representations, pp 7122\u20137134"},{"key":"7031_CR30","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 Conference on Computer Vision and Pattern Recognition, pp 11907\u201311916","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"7031_CR31","doi-asserted-by":"publisher","first-page":"2350033","DOI":"10.1142\/S1469026823500335","volume":"23","author":"Z Liu","year":"2024","unstructured":"Liu Z, Sun B, Bi K (2024) Optimization of yolov7 based on pconv, se attention and wise-iou. Int J Comput Intell Appl 23:2350033\u20132350055","journal-title":"Int J Comput Intell Appl"},{"key":"7031_CR32","doi-asserted-by":"crossref","unstructured":"Tan M, Pang R, Le Q (2020) Efficientdet: scalable and efficient object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 10781\u201310790","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"7031_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.107896","volume":"132","author":"X Zhuang","year":"2024","unstructured":"Zhuang X, Li D, Wang Y, Li K (2024) Military target detection method based on efficientdet and generative adversarial network. Eng Appl Artif Intell 132:107896","journal-title":"Eng Appl Artif Intell"},{"key":"7031_CR34","doi-asserted-by":"publisher","first-page":"3169","DOI":"10.3390\/rs16173169","volume":"16","author":"C Yu","year":"2024","unstructured":"Yu C, Shin Y (2024) Mcg-rtdetr: multi-convolution and context-guided network with cascaded group attention for object detection in unmanned aerial vehicle imagery. Remote Sens 16:3169","journal-title":"Remote Sens"},{"key":"7031_CR35","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Proceedings of the Conference on Neural Information Processing Systems, pp 3762\u20133776"},{"key":"7031_CR36","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, Xu T, Lang X, Ren Z (2023) Mixed local channel attention for object detection. Eng Appl Artif Intell 123:106442","journal-title":"Eng Appl Artif Intell"},{"key":"7031_CR37","unstructured":"Guo M, Lu C, Hou Q, Liu Z, Cheng M, Hu S (2022) SegNeXt: rethinking convolutional attention design for semantic segmentation. In: Proceedings of the Conference on Neural Information Processing Systems, pp 1140\u20131156"},{"key":"7031_CR38","doi-asserted-by":"publisher","first-page":"107445","DOI":"10.1109\/ACCESS.2024.3436709","volume":"12","author":"Y You","year":"2024","unstructured":"You Y, Wang J, Yu Z, Sun Y, Peng Y, Zhang S, Bian S, Wang E, Wu W (2024) A fine-grained detection network model for soldier targets adopting attack action. IEEE Access 12:107445\u2013107458","journal-title":"IEEE Access"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07031-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-07031-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07031-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T01:40:31Z","timestamp":1739842831000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-07031-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,17]]},"references-count":38,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["7031"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-07031-1","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,17]]},"assertion":[{"value":"2 February 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 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 declared that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This study did not involve human or animal subjects, and thus, no ethical approval was required.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}}],"article-number":"510"}}