{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T18:31:43Z","timestamp":1784140303199,"version":"3.55.0"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T00:00:00Z","timestamp":1762387200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T00:00:00Z","timestamp":1762387200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"The Key Laboratory of State Forestry and Grassland Administration on Forestry Ecological Big Data, Southwest Forestry University under Grant","award":["2022-BDG-09"],"award-info":[{"award-number":["2022-BDG-09"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1007\/s40747-025-02117-y","type":"journal-article","created":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T03:26:22Z","timestamp":1762399582000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Robust real-time recognition of drones and birds in complex scenarios: a multimodal data fusion recognize approach"],"prefix":"10.1007","volume":"12","author":[{"given":"Jincan","family":"Zhu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1467-9490","authenticated-orcid":false,"given":"Jian","family":"Rong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weili","family":"Kou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingyang","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peichun","family":"Suo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,6]]},"reference":[{"key":"2117_CR1","doi-asserted-by":"crossref","unstructured":"Afridi S, Hlebowicz K, Cawthorne D, Lundquist UPS (2024) Unveiling the impact of drone noise on wildlife: a crucial research imperative. In: International conference on unmanned aircraft systems (ICUAS) 2024","DOI":"10.1109\/ICUAS60882.2024.10557094"},{"issue":"21","key":"2117_CR2","doi-asserted-by":"publisher","first-page":"8711","DOI":"10.3390\/s23218711","volume":"23","author":"HJ Al Dawasari","year":"2023","unstructured":"Al Dawasari HJ, Bilal M, Moinuddin M, Arshad K, Assaleh K (2023) Deepvision: enhanced drone detection and recognition in visible imagery through deep learning networks. Sensors 23(21):8711","journal-title":"Sensors"},{"key":"2117_CR3","doi-asserted-by":"crossref","unstructured":"Alla I, Olou HB, Loscri V, Levorato M (2024) From sound to sight: audio-visual fusion and deep learning for drone detection. In: Proceedings of the 17th ACM conference on security and privacy in wireless and mobile networks, pp 123\u2013133","DOI":"10.1145\/3643833.3656133"},{"issue":"1","key":"2117_CR4","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/s11760-023-02703-y","volume":"18","author":"SS Aote","year":"2024","unstructured":"Aote SS, Wankhade N, Pardhi A, Misra N, Agrawal H, Potnurwar A (2024) An improved deep learning method for flying object detection and recognition. SIViP 18(1):143\u2013152","journal-title":"SIViP"},{"key":"2117_CR5","doi-asserted-by":"crossref","unstructured":"Cantu\u00a0de Leija A, Mirzadi RE, Randall JM, Portmann MD, Mueller EJ, Gawlik DE (2023) A meta-analysis of disturbance caused by drones on nesting birds. J Field Ornithol. https:\/\/api.semanticscholar.org\/CorpusID:258443690","DOI":"10.5751\/JFO-00259-940203"},{"key":"2117_CR6","unstructured":"Chen Y, Yuan X, Wu R, Wang J, Hou Q, Cheng M-M (2023) Yolo-ms: rethinking multi-scale representation learning for real-time object detection. arXiv preprint. arXiv:2308.05480"},{"key":"2117_CR7","doi-asserted-by":"publisher","unstructured":"Coluccia A, Fascista A, Sommer L, Schumann A, Dimou A, Zarpalas D (2024) The drone-vs-bird detection grand challenge at ICASSP 2023: a review of methods and results. IEEE Open J Signal Process5:766\u2013779. https:\/\/doi.org\/10.1109\/OJSP.2024.3379073","DOI":"10.1109\/OJSP.2024.3379073"},{"key":"2117_CR8","unstructured":"Deng T, Zhou Y, Wu W, Li M, Huang J, Liu S, Song Y, Zuo H, Wang Y, Yue Y (2024) Multi-modal uav detection, classification and tracking algorithm\u2014technical report for CVPR 2024 ug2 challenge. https:\/\/arxiv.org\/abs\/2405.16464"},{"key":"2117_CR9","unstructured":"Deng T, Zhou Y, Wu W, Li M, Huang J, Liu S, Song Y, Zuo H, Wang Y, Yue Y, Wang H, Chen W (2024) Multi-modal UAV detection, classification and tracking algorithm\u2014technical report for CVPR 2024 ug2 challenge. https:\/\/arxiv.org\/abs\/2405.16464"},{"issue":"1","key":"2117_CR10","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1007\/s11042-022-12964-3","volume":"82","author":"Q Dong","year":"2023","unstructured":"Dong Q, Liu Y, Liu X (2023) Drone sound detection system based on feature result-level fusion using deep learning. Multimedia Tools Appl 82(1):149\u2013171","journal-title":"Multimedia Tools Appl"},{"issue":"1","key":"2117_CR11","doi-asserted-by":"publisher","first-page":"19","DOI":"10.23919\/JSEE.2023.000002","volume":"34","author":"J Duan","year":"2023","unstructured":"Duan J, Zhang L, Wu Y, Zhang Y, Zhao Z, Guo X (2023) Classification of birds and drones by exploiting periodical motions in doppler spectrum series. J Syst Eng Electron 34(1):19\u201327","journal-title":"J Syst Eng Electron"},{"key":"2117_CR12","unstructured":"Gao P, Lu J, Li H, Mottaghi R, Kembhavi A (2021) Container: context aggregation network. https:\/\/arxiv.org\/abs\/2106.01401"},{"key":"2117_CR13","unstructured":"Ge Z (2021) Yolox: exceeding yolo series in 2021. arXiv preprint. arXiv:2107.08430"},{"issue":"1","key":"2117_CR14","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1332\/IMLH5791","volume":"2","author":"A Jackman","year":"2023","unstructured":"Jackman A, Millner N, Cunliffe AM, Laumonier Y, Lunstrum E, Paneque-G\u00e1lvez J, Wich SA (2023) Protecting people and wildlife from the potential harms of drone use in biodiversity conservation: interdisciplinary dialogues. Glob Soc Chall J 2(1):68\u201383","journal-title":"Glob Soc Chall J"},{"issue":"14","key":"2117_CR15","doi-asserted-by":"publisher","first-page":"3923","DOI":"10.3390\/s20143923","volume":"20","author":"S Jamil Fawad","year":"2020","unstructured":"Jamil Fawad S, Rahman M, Ullah A, Badnava S, Forsat M, Mirjavadi SS (2020) Malicious UAV detection using integrated audio and visual features for public safety applications. Sensors 20(14):3923","journal-title":"Sensors"},{"key":"2117_CR16","unstructured":"Jocher G (2023) Yolov8: new advances in real-time object detection. GitHub Repository. https:\/\/github.com\/ultralytics\/ultralytics\/tree\/main"},{"key":"2117_CR17","unstructured":"Khanam R, Hussain M (2024) Yolov11: an overview of the key architectural enhancements. arXiv preprint. arXiv:2410.17725"},{"key":"2117_CR18","doi-asserted-by":"crossref","unstructured":"Khazaei E, Esmaeilzehi A, Taha B, Hatzinakos D (2024) CDFL: efficient federated human activity recognition using contrastive learning and deep clustering. https:\/\/arxiv.org\/abs\/2407.12287","DOI":"10.1109\/JSEN.2024.3464416"},{"issue":"14","key":"2117_CR19","doi-asserted-by":"publisher","first-page":"5521","DOI":"10.3390\/en16145521","volume":"16","author":"A Kootala","year":"2023","unstructured":"Kootala A, Mousa A, Pong PW (2023) Drones are endangering energy critical infrastructure, and how we can deal with this. Energies 16(14):5521","journal-title":"Energies"},{"issue":"3","key":"2117_CR20","doi-asserted-by":"publisher","first-page":"721","DOI":"10.3390\/s25030721","volume":"25","author":"M Larrat","year":"2025","unstructured":"Larrat M, Sales C (2025) Classification of flying drones using millimeter-wave radar: comparative analysis of algorithms under noisy conditions. Sensors 25(3):721","journal-title":"Sensors"},{"key":"2117_CR21","unstructured":"Li C, Li L, Jiang H, Weng K, Geng Y, Li L, Ke Z, Li Q, Cheng M, Nie W et\u00a0al. (2022) Yolov6: a single-stage object detection framework for industrial applications. arXiv preprint. arXiv:2209.02976"},{"key":"2117_CR22","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Doll\u00e1r P, Zitnick CL (2014) Microsoft coco: common objects in context. In: Computer vision-ECCV, 13th European conference, Zurich, Switzerland, September 6\u201312, 2014, proceedings, part v 13. Springer, pp 740\u2013755","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"2117_CR23","doi-asserted-by":"publisher","first-page":"3121","DOI":"10.3390\/rs16173121","volume":"16","author":"Q Liu","year":"2024","unstructured":"Liu Q (2024) A cross-modal semantic alignment and feature fusion method for bionic drone and bird recognition. Remote Sens 16:3121","journal-title":"Remote Sens"},{"key":"2117_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109741","volume":"255","author":"Z Liu","year":"2022","unstructured":"Liu Z, Cheng J, Liu L, Ren Z, Zhang Q, Song C (2022) Dual-stream cross-modality fusion transformer for rgb-d action recognition. Knowl-Based Syst 255:109741","journal-title":"Knowl-Based Syst"},{"key":"2117_CR25","unstructured":"Lyu C, Zhang W, Huang H, Zhou Y, Wang Y, Liu Y, Zhang S, Chen K (2022) Rtmdet: an empirical study of designing real-time object detectors. arXiv preprint. arXiv:2212.07784"},{"key":"2117_CR26","doi-asserted-by":"crossref","unstructured":"Mehta V, Dadboud F, Bolic M, Mantegh I (2023) A deep learning approach for drone detection and classification using radar and camera sensor fusion. In: IEEE sensors applications symposium (SAS). IEEE, pp 1\u20136","DOI":"10.1109\/DASC58513.2023.10311246"},{"key":"2117_CR27","doi-asserted-by":"crossref","unstructured":"Misbah M, Khan MU, Yang Z, Kaleem Z (2023) Tf-net: deep learning empowered tiny feature network for night-time UAV detection. In: International conference on wireless and satellite systems. Springer, pp 3\u201318","DOI":"10.1007\/978-3-031-34851-8_1"},{"key":"2117_CR28","unstructured":"Qian C, Xing S, Li S, Zhao Y, Tu Z (2025) Decalign: hierarchical cross-modal alignment for decoupled multimodal representation learning. https:\/\/arxiv.org\/abs\/2503.11892"},{"issue":"5","key":"2117_CR29","doi-asserted-by":"publisher","first-page":"879","DOI":"10.3390\/rs16050879","volume":"16","author":"MH Rahman","year":"2024","unstructured":"Rahman MH, Sejan MAS, Aziz MA, Tabassum R, Baik J-I, Song H-K (2024) A comprehensive survey of unmanned aerial vehicles detection and classification using machine learning approach: challenges, solutions, and future directions. Remote Sens 16(5):879","journal-title":"Remote Sens"},{"issue":"1","key":"2117_CR30","doi-asserted-by":"publisher","first-page":"125","DOI":"10.3390\/s24010125","volume":"24","author":"U Seidaliyeva","year":"2023","unstructured":"Seidaliyeva U, Ilipbayeva L, Taissariyeva K, Smailov N, Matson ET (2023) Advances and challenges in drone detection and classification techniques: a state-of-the-art review. Sensors 24(1):125","journal-title":"Sensors"},{"key":"2117_CR31","doi-asserted-by":"crossref","unstructured":"Shafik W (2023) Cyber security perspectives in public spaces: drone case study. In: Handbook of research on cybersecurity risk in contemporary business systems. IGI Global, USA, pp 79\u201397","DOI":"10.4018\/978-1-6684-7207-1.ch004"},{"issue":"2","key":"2117_CR32","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1504\/IJIE.2024.137679","volume":"11","author":"KK Shenoy","year":"2024","unstructured":"Shenoy KK, Tyagi D (2024) Unmanned aircraft system safety, security, and regulation in urban aviation ecosystems. Int J Intell Enterprise 11(2):157\u2013175","journal-title":"Int J Intell Enterprise"},{"issue":"5","key":"2117_CR33","doi-asserted-by":"publisher","first-page":"314","DOI":"10.3390\/drones7050314","volume":"7","author":"F \u0160kult\u00e9ty","year":"2023","unstructured":"\u0160kult\u00e9ty F, Bujna E, Janovec M, Kandera B (2023) Noise impact assessment of UAS operation in urbanised areas: field measurements and a simulation. Drones 7(5):314","journal-title":"Drones"},{"key":"2117_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.trd.2023.103686","volume":"118","author":"Q Tan","year":"2023","unstructured":"Tan Q, Bian H, Guo J, Zhou P, Lo HK, Zhong S, Zhang X (2023) Virtual flight simulation of delivery drone noise in the urban residential community. Transp Res D Transp Environ 118:103686","journal-title":"Transp Res D Transp Environ"},{"key":"2117_CR35","doi-asserted-by":"crossref","unstructured":"Tan M, Pang R, Le QV (2020) Efficientdet: scalable and efficient object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 10781\u201310790","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"2117_CR36","doi-asserted-by":"crossref","unstructured":"Wang C-Y, Bochkovskiy A, Liao H-YM (2023) Yolov7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 7464\u20137475","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"2117_CR37","doi-asserted-by":"crossref","unstructured":"Wang A, Chen H, Liu L, Chen K, Lin Z, Han J, Ding G (2024) Yolov10: real-time end-to-end object detection. arXiv preprint. arXiv:2405.14458","DOI":"10.52202\/079017-3429"},{"key":"2117_CR38","unstructured":"Wang C, He W, Nie Y, Guo J, Liu C, Wang Y, Han K (2024) Gold-yolo: efficient object detector via gather-and-distribute mechanism. Adv Neural Inf Process Syst 36"},{"key":"2117_CR39","doi-asserted-by":"crossref","unstructured":"Wang C-Y, Yeh I-H, Mark\u00a0Liao H-Y (2025) Yolov9: learning what you want to learn using programmable gradient information. In: European conference on computer vision. Springer, pp 1\u201321","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"2117_CR40","doi-asserted-by":"publisher","unstructured":"Wang G, Jiang Q, Jin X, Wozniak M, Wang P, Yao S (2025) RTM-UAVDET: a real-time multimodal UAV detector. IEEE Trans Aerosp Electron Syst 61(1):473\u2013489 https:\/\/doi.org\/10.1109\/TAES.2024.3446460","DOI":"10.1109\/TAES.2024.3446460"},{"key":"2117_CR41","doi-asserted-by":"publisher","DOI":"10.1049\/csy2.70011","author":"Y Wu","year":"2025","unstructured":"Wu Y, Cui J, Niu K, Lu Y, Cheng L, Cai S, Xu C (2025) Rmf-ed: real-time multimodal fusion for enhanced target detection in low-light environments. IET Cybersyst Robot. https:\/\/doi.org\/10.1049\/csy2.70011","journal-title":"IET Cybersyst Robot"},{"key":"2117_CR42","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2024.3510428","author":"Z Xiao","year":"2025","unstructured":"Xiao Z, Tong H (2025) Federated contrastive learning with feature-based distillation for human activity recognition. IEEE Trans Comput Soc Syst. https:\/\/doi.org\/10.1109\/TCSS.2024.3510428","journal-title":"IEEE Trans Comput Soc Syst"},{"key":"2117_CR43","doi-asserted-by":"publisher","first-page":"16589","DOI":"10.1109\/TNNLS.2025.3556317","volume":"36","author":"Z Xiao","year":"2025","unstructured":"Xiao Z, Xing H, Qu R, Li H, Cheng X, Xu L, Feng L, Wan Q (2025) Heterogeneous mutual knowledge distillation for wearable human activity recognition. IEEE Trans Neural Netw Learn Syst 36:16589\u201316603","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2117_CR44","unstructured":"Xu X, Jiang Y, Chen W, Huang Y, Zhang Y, Sun X (2022) Damo-yolo: a report on real-time object detection design. arXiv preprint. arXiv:2211.15444"},{"key":"2117_CR45","unstructured":"Xu S, Wang X, Lv W, Chang Q, Cui C, Deng K, Wang G, Dang Q, Wei S, Du Y et\u00a0al. (2022) Pp-yoloe: an evolved version of yolo. arXiv preprint. arXiv:2203.16250"},{"key":"2117_CR46","doi-asserted-by":"crossref","unstructured":"Yablokova A, Kovalev D, Kovalev I, Podoplelova V, Astanakulov K (2024) Environmental safety problems of swarm use of uavs in precision agriculture. In: E3S web of conferences. EDP Sciences, vol 471, p 04018","DOI":"10.1051\/e3sconf\/202447104018"},{"issue":"7","key":"2117_CR47","doi-asserted-by":"publisher","first-page":"589","DOI":"10.3390\/e26070589","volume":"26","author":"Q Zhang","year":"2024","unstructured":"Zhang Q, Jin G, Zhu Y, Wei H, Chen Q (2024) Bpt-plr: a balanced partitioning and training framework with pseudo-label relaxed contrastive loss for noisy label learning. Entropy 26(7):589","journal-title":"Entropy"},{"key":"2117_CR48","doi-asserted-by":"crossref","unstructured":"Zhang Q, Zhu Y, Cordeiro FR, Chen Q (2025) Psscl: a progressive sample selection framework with contrastive loss designed for noisy labels. Pattern Recogn 161:111284","DOI":"10.1016\/j.patcog.2024.111284"}],"container-title":["Complex & Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-02117-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-025-02117-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-02117-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T11:49:21Z","timestamp":1769773761000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-025-02117-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,6]]},"references-count":48,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["2117"],"URL":"https:\/\/doi.org\/10.1007\/s40747-025-02117-y","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,6]]},"assertion":[{"value":"23 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 November 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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Consent for publication has been obtained from all relevant parties, including participants and\/or data contributors, for the materials presented in this study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The materials used in this study are not publicly available at the moment. However, they can be provided upon reasonable request by contacting the corresponding author at: swordrong@swfu.edu.cn.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Materials availability"}},{"value":"The code for this project is available upon request.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}}],"article-number":"8"}}