{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T18:44:42Z","timestamp":1772822682370,"version":"3.50.1"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"22","license":[{"start":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T00:00:00Z","timestamp":1748476800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T00:00:00Z","timestamp":1748476800000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1007\/s00521-025-11305-3","type":"journal-article","created":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T14:53:30Z","timestamp":1748530410000},"page":"17231-17247","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["YOLOv11n-UAV: improved YOLOv11n model for detecting small UAVs using infrared images on complex backgrounds"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9911-3512","authenticated-orcid":false,"given":"Phat T.","family":"Nguyen","sequence":"first","affiliation":[]},{"given":"Long H.","family":"Nguyen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,29]]},"reference":[{"key":"11305_CR1","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.isprsjprs.2014.02.013","volume":"92","author":"I Colomina","year":"2014","unstructured":"Colomina I, Molina P (2014) Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS J Photogramm R Sens 92:79\u201397","journal-title":"ISPRS J Photogramm R Sens"},{"key":"11305_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-025-11002-1","author":"NT Phat","year":"2025","unstructured":"Phat NT, Giang NL, Duy BD (2025) GAN-UAV-YOLOv10s: improved YOLOv10s network for detecting small UAV targets in mountainous conditions based on infrared image data. Neural Computi Appl. https:\/\/doi.org\/10.1007\/s00521-025-11002-1","journal-title":"Neural Computi Appl"},{"key":"11305_CR3","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1007\/s10462-025-11150-9","volume":"58","author":"W Hua","year":"2025","unstructured":"Hua W, Chen Q (2025) A survey of small object detection based on deep learning in aerial images. Artif Intell Rev 58:162. https:\/\/doi.org\/10.1007\/s10462-025-11150-9","journal-title":"Artif Intell Rev"},{"issue":"12","key":"11305_CR4","doi-asserted-by":"publisher","first-page":"4996","DOI":"10.1109\/TIP.2013.2281420","volume":"22","author":"C Gao","year":"2013","unstructured":"Gao C, Meng D, Yang Y, Wang Y, Zhou X, Hauptmann AG (2013) Infrared patch-image model for small target detection in a single image. IEEE Trans Image Process 22(12):4996\u20135009","journal-title":"IEEE Trans Image Process"},{"issue":"8","key":"11305_CR5","first-page":"3752","volume":"10","author":"Y Dai","year":"2017","unstructured":"Dai Y, Wu Y (2017) Reweighted infrared patch-tensor model with both nonlocal and local priors for single-frame small target detection. IEEE J Selected Top Appl Earth Observat R Sens 10(8):3752\u20133767","journal-title":"IEEE J Selected Top Appl Earth Observat R Sens"},{"issue":"2","key":"11305_CR6","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1109\/LGRS.2018.2872166","volume":"16","author":"S Yao","year":"2019","unstructured":"Yao S, Chang Y, Qin X (2019) A coarse-to-fine method for infrared small target detection. IEEE Geosci Remote Sens Lett 16(2):256\u2013260","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"11305_CR7","doi-asserted-by":"crossref","unstructured":"Svanstr\u00f6m F, Englund C, Alonso-Fernandez F (2021) Real-time drone detection and tracking with visible, thermal and acoustic sensors. In Proceedings of the 2020 25th international conference on pattern recognition (ICPR), Milan, Italy, 10-15 January 2021; pp. 7265-7272","DOI":"10.1109\/ICPR48806.2021.9413241"},{"key":"11305_CR8","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1109\/JPROC.2023.3238524","volume":"111","author":"Z Zou","year":"2023","unstructured":"Zou Z, Chen K, Shi Z, Guo Y, Ye J (2023) Object detection in 20 years: a survey. Proc IEEE 111:257\u2013276","journal-title":"Proc IEEE"},{"key":"11305_CR9","doi-asserted-by":"publisher","first-page":"1520","DOI":"10.1049\/ipr2.12735","volume":"17","author":"M Tang","year":"2023","unstructured":"Tang M, Liang K, Qiu J (2023) Small insulator target detection based on multi-feature fusion. IET Image Proc 17:1520\u20131533","journal-title":"IET Image Proc"},{"key":"11305_CR10","doi-asserted-by":"publisher","first-page":"1590","DOI":"10.3390\/s24051590","volume":"24","author":"L Zhang","year":"2024","unstructured":"Zhang L, Xu W, Shen C, Huang Y (2024) Vision-based on-road nighttime vehicle detection and tracking using improved HOG features. Sensors 24:1590","journal-title":"Sensors"},{"issue":"10","key":"11305_CR11","doi-asserted-by":"publisher","first-page":"6549","DOI":"10.1007\/s00521-018-3486-1","volume":"31","author":"J Leng","year":"2019","unstructured":"Leng J, Liu Y (2019) An enhanced SSD with feature fusion and visual reasoning for object detection. Neural Comput Appl 31(10):6549\u20136558","journal-title":"Neural Comput Appl"},{"key":"11305_CR12","doi-asserted-by":"publisher","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg, AC. SSD: Single shot MultiBox detector. In Leibe, B.; Matas, J.; Sebe, N.; Welling, M. (Eds.) Computer Vision-ECCV 2016, Springer International Publishing, Cham, pp. 21-37. https:\/\/doi.org\/10.1007\/978-3-319-46448-0-2","DOI":"10.1007\/978-3-319-46448-0-2"},{"issue":"2","key":"11305_CR13","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1109\/TPAMI.2019.2922181","volume":"42","author":"Z Shen","year":"2020","unstructured":"Shen Z, Liu Z, Li J, Jiang YG, Chen Y, Xue X (2020) Object detection from scratch with deep supervision. IEEE Trans Pattern Anal Mach Intell 42(2):398\u2013412. https:\/\/doi.org\/10.1109\/TPAMI.2019.2922181","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11305_CR14","doi-asserted-by":"publisher","unstructured":"Newell A, Yang K, Deng J. Stacked Hourglass networks for human pose estimation. In Proceedings of the 2016 European conference on computer vision (ECCV), Springer International Publishing, Cham, pp. 483-499. https:\/\/doi.org\/10.1007\/978-3-319-46484-8_29","DOI":"10.1007\/978-3-319-46484-8_29"},{"issue":"13","key":"11305_CR15","doi-asserted-by":"publisher","first-page":"10469","DOI":"10.1007\/s00521-021-06830-w","volume":"34","author":"I Rodriguez-Conde","year":"2022","unstructured":"Rodriguez-Conde I, Campos C, Fdez-Riverola F (2022) Optimized convolutional neural network architectures for efficient on device vision based object detection. Neural Comput Appl 34(13):10469\u201310501","journal-title":"Neural Comput Appl"},{"key":"11305_CR16","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 39:1137\u20131149","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11305_CR17","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Doll\u00e1r P, Girshick R (2017) Mask R-CNN. In Proceedings of the 2017 IEEE International conference on computer vision (ICCV), Venice, Italy, 22-29 October pp. 2980-2988","DOI":"10.1109\/ICCV.2017.322"},{"key":"11305_CR18","doi-asserted-by":"crossref","unstructured":"Cheng B, Wei Y, Shi H, Feris R, Xiong J, Huang T (2018) Revisiting RCNN: On Awakening the Classification Power of Faster RCNN. In Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 8-14 September pp. 473-490","DOI":"10.1007\/978-3-030-01267-0_28"},{"key":"11305_CR19","first-page":"4065734","volume":"2022","author":"H Zhai","year":"2022","unstructured":"Zhai H, Zhang Y (2022) Target detection of low-altitude UAV based on improved YOLOv3 network. J. Robot. 2022:4065734","journal-title":"J. Robot."},{"issue":"11","key":"11305_CR20","doi-asserted-by":"publisher","first-page":"680","DOI":"10.3390\/drones8110680","volume":"8","author":"R Hakani","year":"2024","unstructured":"Hakani R, Rawat A (2024) Edge computing-driven real-time drone detection using YOLOv9 and NVIDIA Jetson Nano. Drones 8(11):680. https:\/\/doi.org\/10.3390\/drones8110680","journal-title":"Drones"},{"issue":"22","key":"11305_CR21","doi-asserted-by":"publisher","first-page":"7148","DOI":"10.3390\/s24227148","volume":"24","author":"P Jiang","year":"2024","unstructured":"Jiang P, Yang X, Wan Y, Zeng T, Nie M, Liu Z (2024) DRBD-YOLOv8: a lightweight and efficient anti-UAV detection model. Sensors 24(22):7148. https:\/\/doi.org\/10.3390\/s24227148","journal-title":"Sensors"},{"issue":"17","key":"11305_CR22","doi-asserted-by":"publisher","first-page":"3664","DOI":"10.3390\/electronics12173664","volume":"12","author":"X Zhai","year":"2023","unstructured":"Zhai X, Huang Z, Li T, Liu H, Wang S (2023) YOLO-Drone: an optimized YOLOv8 network for tiny UAV object detection. Electronics 12(17):3664. https:\/\/doi.org\/10.3390\/electronics12173664","journal-title":"Electronics"},{"issue":"11","key":"11305_CR23","doi-asserted-by":"publisher","first-page":"681","DOI":"10.3390\/drones8110681","volume":"8","author":"Y Zhao","year":"2024","unstructured":"Zhao Y, Wang L, Lei G, Guo C, Ma Q (2024) Lightweight UAV small target detection and perception based on improved YOLOv8-E. Drones 8(11):681. https:\/\/doi.org\/10.3390\/drones8110681","journal-title":"Drones"},{"issue":"6","key":"11305_CR24","doi-asserted-by":"publisher","first-page":"393","DOI":"10.3390\/drones7060393","volume":"7","author":"S Yuan","year":"2023","unstructured":"Yuan S, Sun B, Zuo Z, Huang H, Wu P, Li C, Dang Z, Zhao Z (2023) IRSDD-YOLOv5: focusing on the infrared detection of small drones. Drones 7(6):393. https:\/\/doi.org\/10.3390\/drones7060393","journal-title":"Drones"},{"key":"11305_CR25","unstructured":"Khanam R, Hussain M (2024) Yolov11: An overview of the key architectural enhancements. arXiv preprint arXiv:2410.17725"},{"issue":"1","key":"11305_CR26","doi-asserted-by":"publisher","first-page":"11","DOI":"10.3390\/drones9010011","volume":"9","author":"Y Gao","year":"2024","unstructured":"Gao Y, Xin Y, Yang H, Wang Y (2024) A lightweight anti-unmanned aerial vehicle detection method based on improved YOLOv11. Drones 9(1):11","journal-title":"Drones"},{"key":"11305_CR27","doi-asserted-by":"crossref","unstructured":"Nguyen PT, Nguyen GL, Bui DD (2025) LW-UAV-YOLOv10: A Lightweight Model for Small UAV Detection on Infrared Data Based on YOLOv10. Geomatica, 100049","DOI":"10.1016\/j.geomat.2025.100049"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11305-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11305-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11305-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T16:16:14Z","timestamp":1757175374000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11305-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,29]]},"references-count":27,"journal-issue":{"issue":"22","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["11305"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11305-3","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,29]]},"assertion":[{"value":"12 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 May 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 that they do not have any conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This research does not involve any human or animal activities. All authors have checked and agreed to the submission.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}