{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T11:04:44Z","timestamp":1773486284257,"version":"3.50.1"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T00:00:00Z","timestamp":1768608000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T00:00:00Z","timestamp":1768608000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62261060"],"award-info":[{"award-number":["62261060"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Pattern Anal Applic"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1007\/s10044-026-01610-5","type":"journal-article","created":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T02:20:19Z","timestamp":1768616419000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["YOLO-LIRTU: a lightweight infrared real-time UAV detection framework"],"prefix":"10.1007","volume":"29","author":[{"given":"Hao","family":"Yu","sequence":"first","affiliation":[]},{"given":"Huan","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Wangming","family":"Lan","sequence":"additional","affiliation":[]},{"given":"Qian","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Hongyue","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Jin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,17]]},"reference":[{"key":"1610_CR1","first-page":"1","volume":"20","author":"T Ma","year":"2023","unstructured":"Ma T, Yang Z, Liu B, Sun S (2023) A lightweight infrared small target detection network based on target multiscale context. IEEE Geosci Remote Sens Lett 20:1\u20135","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"1610_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109788","volume":"143","author":"R Kou","year":"2023","unstructured":"Kou R, Wang C, Peng Z, Zhao Z, Chen Y, Han J, Huang F, Yu Y, Fu Q (2023) Infrared small target segmentation networks: a survey. Pattern Recogn 143:109788","journal-title":"Pattern Recogn"},{"issue":"2","key":"1610_CR3","doi-asserted-by":"publisher","first-page":"2320","DOI":"10.1109\/TVT.2023.3320176","volume":"73","author":"W-C Jin","year":"2024","unstructured":"Jin W-C, Kim K, Choi J-W (2024) Adaptive beam control considering location inaccuracy for anti-uav systems. IEEE Trans Veh Technol 73(2):2320\u20132331","journal-title":"IEEE Trans Veh Technol"},{"key":"1610_CR4","doi-asserted-by":"publisher","first-page":"7","DOI":"10.3390\/drones8070332","volume":"8","author":"Y Huang","year":"2024","unstructured":"Huang Y, Qu J, Wang H, Yang J (2024) An all-time detection algorithm for uav images in urban low altitude. Drones 8:7","journal-title":"Drones"},{"key":"1610_CR5","doi-asserted-by":"crossref","unstructured":"Wu A, Liu R, Han Y, Zhu L, Yang Y (2021) Vector-decomposed disentanglement for domain-invariant object detection. In: IEEE\/CVF international conference on computer vision (ICCV) 2021:9322\u20139331","DOI":"10.1109\/ICCV48922.2021.00921"},{"key":"1610_CR6","doi-asserted-by":"publisher","first-page":"3281","DOI":"10.1109\/JSTARS.2024.3523418","volume":"18","author":"Q Wang","year":"2025","unstructured":"Wang Q, Jin P, Wu Y, Zhou L, Shen T (2025) Infrared image enhancement: a review. IEEE J Select Top Appl Earth Observ Remote Sens 18:3281\u20133299","journal-title":"IEEE J Select Top Appl Earth Observ Remote Sens"},{"issue":"6","key":"1610_CR7","doi-asserted-by":"publisher","first-page":"10486","DOI":"10.1109\/JSEN.2025.3534277","volume":"25","author":"W Li","year":"2025","unstructured":"Li W, Li J, Cao B, Zhu J, Tian M (2025) Faa-yolo: a method for defects detection of small infrared targets in photovoltaic modules. IEEE Sens J 25(6):10486\u201310497","journal-title":"IEEE Sens J"},{"key":"1610_CR8","doi-asserted-by":"publisher","first-page":"12126","DOI":"10.1109\/ACCESS.2024.3355157","volume":"12","author":"J Wu","year":"2024","unstructured":"Wu J, He Y, Zhao J (2024) An infrared target images recognition and processing method based on the fuzzy comprehensive evaluation. IEEE Access 12:12126\u201312137","journal-title":"IEEE Access"},{"issue":"12","key":"1610_CR9","doi-asserted-by":"publisher","first-page":"19517","DOI":"10.1109\/JSEN.2024.3394956","volume":"24","author":"X Luo","year":"2024","unstructured":"Luo X, Luo S, Chen M, Zhao G, He C, Wu H (2024) Mbformer-yolo: multibranch adaptive spatial feature detection network for small infrared object detection. IEEE Sens J 24(12):19517\u201319530","journal-title":"IEEE Sens J"},{"issue":"5","key":"1610_CR10","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1109\/31.1775","volume":"35","author":"MM Hadhoud","year":"1988","unstructured":"Hadhoud MM, Thomas DW (1988) The two-dimensional adaptive lms (tdlms) algorithm. IEEE Trans Circ Syst 35(5):485\u2013494","journal-title":"IEEE Trans Circ Syst"},{"key":"1610_CR11","doi-asserted-by":"crossref","unstructured":"Widrow B, McCool J, Larimore MG, Johnson CR (1977) Stationary and nonstationary learning characteristics of the lms adaptive filter. In: Aspects of signal processing: with emephasis on underwater acoustics part 1 proceedings of the NATO advanced study institute held at Portovenere, La Spezia, Italy 30 August\u201311 September 1976. Springer, 355\u2013393","DOI":"10.1007\/978-94-010-1223-2_23"},{"issue":"1","key":"1610_CR12","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1109\/TGRS.2013.2242477","volume":"52","author":"CP Chen","year":"2013","unstructured":"Chen CP, Li H, Wei Y, Xia T, Tang YY (2013) A local contrast method for small infrared target detection. IEEE Trans Geosci Remote Sens 52(1):574\u2013581","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1610_CR13","unstructured":"Redmon J, Farhadi A (2018) Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767,"},{"key":"1610_CR14","doi-asserted-by":"crossref","unstructured":"Adarsh P, Rathi P, Kumar M (2020) Yolo v3-tiny: object detection and recognition using one stage improved model. In: 6th international conference on advanced computing and communication systems (ICACCS). IEEE 2020:687\u2013694","DOI":"10.1109\/ICACCS48705.2020.9074315"},{"key":"1610_CR15","unstructured":"Ge Z, Liu S, Wang F, Li Z, Sun J (2021) Yolox: Exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430"},{"key":"1610_CR16","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, 7464\u20137475","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"1610_CR17","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.cmpb.2018.01.017","volume":"157","author":"MA Al-Masni","year":"2018","unstructured":"Al-Masni MA, Al-Antari MA, Park J-M, Gi G, Kim T-Y, Rivera P, Valarezo E, Choi M-T, Han S-M, Kim T-S (2018) Simultaneous detection and classification of breast masses in digital mammograms via a deep learning yolo-based cad system. Comput Methods Progr Biomed 157:85\u201394","journal-title":"Comput Methods Progr Biomed"},{"key":"1610_CR18","doi-asserted-by":"crossref","unstructured":"Novak B, Ili\u0107 V, Pavkovi\u0107 B (2020) Yolov3 algorithm with additional convolutional neural network trained for traffic sign recognition. In: Zooming innovation in consumer technologies conference (ZINC). IEEE 2020:165\u2013168","DOI":"10.1109\/ZINC50678.2020.9161446"},{"key":"1610_CR19","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1016\/j.compag.2019.01.012","volume":"157","author":"Y Tian","year":"2019","unstructured":"Tian Y, Yang G, Wang Z, Wang H, Li E, Liang Z (2019) Apple detection during different growth stages in orchards using the improved yolo-v3 model. Comput Electron Agric 157:417\u2013426","journal-title":"Comput Electron Agric"},{"key":"1610_CR20","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Doll\u00e1r P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2117\u20132125","DOI":"10.1109\/CVPR.2017.106"},{"key":"1610_CR21","doi-asserted-by":"crossref","unstructured":"Liu S, Qi L, Qin H, Shi J, Jia J (2018) Path aggregation network for instance segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 8759\u20138768","DOI":"10.1109\/CVPR.2018.00913"},{"key":"1610_CR22","doi-asserted-by":"crossref","unstructured":"Ma N, Zhang X, Zheng H-T, Sun J (2018) Shufflenet v2: practical guidelines for efficient cnn architecture design. In: Proceedings of the European conference on computer vision (ECCV), 116\u2013131","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"1610_CR23","doi-asserted-by":"crossref","unstructured":"Zhao Y, Lv W, Xu S, Wei J, Wang G, Dang Q, Liu Y, Chen J (2024) Detrs beat yolos on real-time object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, 16965\u201316974","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"1610_CR24","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\/CVF conference on computer vision and pattern recognition 1580\u20131589","DOI":"10.1109\/CVPR42600.2020.00165"},{"issue":"12","key":"1610_CR25","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"},{"key":"1610_CR26","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1007\/s10762-008-9410-5","volume":"30","author":"X Mao","year":"2009","unstructured":"Mao X, Diao W-H (2009) Criterion to evaluate the quality of infrared small target images. J Infrared Millim Terahertz Waves 30:56\u201364","journal-title":"J Infrared Millim Terahertz Waves"},{"key":"1610_CR27","doi-asserted-by":"crossref","unstructured":"Cho H, Priemer R (1994) Automatic step size adjustment of the two-dimensional lms algorithm. In: Proceedings of 1994 37th midwest symposium on circuits and systems, vol\u00a02. IEEE, pp 864\u2013867","DOI":"10.1109\/MWSCAS.1994.518950"},{"issue":"3","key":"1610_CR28","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1587\/elex.7.112","volume":"7","author":"T-W Bae","year":"2010","unstructured":"Bae T-W, Kim Y-C, Ahn S-H, Sohng K-I (2010) A novel two-dimensional lms (tdlms) using sub-sampling mask and step-size index for small target detection. IEICE Electr Expr 7(3):112\u2013117","journal-title":"IEICE Electr Expr"},{"key":"1610_CR29","doi-asserted-by":"crossref","unstructured":"Deshpande SD, Er MH, Venkateswarlu R, Chan P (1999) Max-mean and max-median filters for detection of small targets. In: Signal and data processing of small targets, vol 3809. SPIE 1999:74\u201383","DOI":"10.1117\/12.364049"},{"key":"1610_CR30","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1016\/j.patcog.2016.04.002","volume":"58","author":"Y Wei","year":"2016","unstructured":"Wei Y, You X, Li H (2016) Multiscale patch-based contrast measure for small infrared target detection. Pattern Recogn 58:216\u2013226","journal-title":"Pattern Recogn"},{"key":"1610_CR31","doi-asserted-by":"crossref","unstructured":"Tom VT, Peli T, Leung M, Bondaryk JE (1993) Morphology-based algorithm for point target detection in infrared backgrounds. In: Signal and data processing of small targets, vol 1954. SPIE 1993:2\u201311","DOI":"10.1117\/12.157758"},{"issue":"7","key":"1610_CR32","doi-asserted-by":"publisher","first-page":"076401","DOI":"10.1117\/1.2759236","volume":"46","author":"P Zhang","year":"2007","unstructured":"Zhang P, Li J (2007) Neural-network-based single-frame detection of dim spot target in infrared images. Opt Eng 46(7):076401\u2013076401","journal-title":"Opt Eng"},{"issue":"2\u20133","key":"1610_CR33","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1016\/j.infrared.2011.12.002","volume":"55","author":"X Bai","year":"2012","unstructured":"Bai X, Zhou F, Xue B (2012) Infrared dim small target enhancement using toggle contrast operator. Infrared Phys Technol 55(2\u20133):177\u2013182","journal-title":"Infrared Phys Technol"},{"key":"1610_CR34","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition 580\u2013587","DOI":"10.1109\/CVPR.2014.81"},{"key":"1610_CR35","doi-asserted-by":"crossref","unstructured":"Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision, 1440\u20131448","DOI":"10.1109\/ICCV.2015.169"},{"issue":"6","key":"1610_CR36","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2016","unstructured":"Ren S, He K, Girshick R, Sun J (2016) Faster r-cnn: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137\u20131149","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1610_CR37","doi-asserted-by":"crossref","unstructured":"Cai Z, Vasconcelos N (2018) Cascade r-cnn: delving into high quality object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 6154\u20136162","DOI":"10.1109\/CVPR.2018.00644"},{"key":"1610_CR38","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Goyal P, Girshick R, He K, Doll\u00e1r P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision 2980\u20132988","DOI":"10.1109\/ICCV.2017.324"},{"issue":"1","key":"1610_CR39","doi-asserted-by":"publisher","first-page":"70","DOI":"10.20517\/ir.2025.05","volume":"5","author":"H Duan","year":"2025","unstructured":"Duan H, Shi F, Gao B, Zhou Y, Cui Q (2025) A novel real-time intelligent detector for monitoring UAVs in live-line operation on 10 kV distribution networks. Intell Robot 5(1):70\u201387","journal-title":"Intell Robot"},{"issue":"3","key":"1610_CR40","first-page":"662","volume":"5","author":"X Chen","year":"2025","unstructured":"Chen X, Yang R, Wu Y, Zhang H, Ranjitkar P, Postolache O, Wang Z (2025) Towards intelligent shipping: Image-enhanced ship detection and situation analysis in low-light scenes. Intell Robot 5(3):662\u2013678","journal-title":"Intell Robot"},{"key":"1610_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2025.104134","volume":"237","author":"M Bakirci","year":"2025","unstructured":"Bakirci M (2025) Advanced aerial monitoring and vehicle classification for intelligent transportation systems with YOLOv8 variants. J Netw Comput Appl 237:104134","journal-title":"J Netw Comput Appl"},{"key":"1610_CR42","doi-asserted-by":"crossref","unstructured":"Li M, Lan J, Zhang Y, Huang K (2025) LAST-Net: local adaptivity spatial transformer network for multi-object detection in UAV remote sensing thermal infrared imagery. IEEE Trans Geosci Remote Sens","DOI":"10.1109\/TGRS.2025.3562966"},{"key":"1610_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2025.110488","volume":"149","author":"MS Ali","year":"2025","unstructured":"Ali MS, Latif A, Anwar MW, Ashraf MH (2025) Multiscale self-attention for unmanned Ariel vehicle-based infrared thermal images detection. Eng Appl Artif Intell 149:110488","journal-title":"Eng Appl Artif Intell"},{"key":"1610_CR44","doi-asserted-by":"crossref","unstructured":"Peng Y, Wang J, Wang W, Liu L, Atiquzzaman M, Guizani M, Dustdar S (2025) An efficient multi-band infrared small objects detection approach for low-altitude artificial intelligence of thing. IEEE Int Things J","DOI":"10.1109\/JIOT.2025.3544258"},{"key":"1610_CR45","doi-asserted-by":"crossref","unstructured":"Fang H, Wang X, Li Z, Wang L, Li Q, Chang Y, Yan L (2025) Detection-friendly nonuniformity correction: A union framework for infrared UAV target detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), 11898\u201311907,","DOI":"10.1109\/CVPR52734.2025.01111"},{"key":"1610_CR46","doi-asserted-by":"crossref","unstructured":"Fang H, Ding L, Wang X, Chang Y, Yan L, Liu L, Fang J (2024) SCINet: spatial and contrast interactive super-resolution assisted infrared UAV target detection. IEEE Trans Geosci Rem Sens","DOI":"10.1109\/TGRS.2024.3471786"},{"key":"1610_CR47","doi-asserted-by":"crossref","unstructured":"Liu B, Jiang Q, Wang P, Yao S, Zhou W, Jin X (2025) IRMSD-YOLO: multiscale dilated network with inverted residuals for infrared small target detection. IEEE Sens J","DOI":"10.1109\/JSEN.2025.3546966"},{"key":"1610_CR48","unstructured":"Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861,"},{"key":"1610_CR49","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"1610_CR50","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"key":"1610_CR51","doi-asserted-by":"crossref","unstructured":"Howard A, Sandler M, Chu G, Chen L-C, Chen B, Tan M, Wang W, Zhu Y, Pang R, Vasudevan V et al (2019) Searching for mobilenetv3. In: Proceedings of the IEEE\/CVF international conference on computer vision 1314\u20131324","DOI":"10.1109\/ICCV.2019.00140"},{"key":"1610_CR52","doi-asserted-by":"crossref","unstructured":"Zhang X, Zhou X, Lin M, Sun J (2018) Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE conference on computer vision and pattern recognition 6848\u20136856","DOI":"10.1109\/CVPR.2018.00716"},{"key":"1610_CR53","unstructured":"Tan M, Le Q (2019) Efficientnet: rethinking model scaling for convolutional neural networks. In: International conference on machine learning. PMLR, 6105\u20136114"},{"key":"1610_CR54","doi-asserted-by":"crossref","unstructured":"Mehta S, Rastegari M, Caspi A, Shapiro L, Hajishirzi H (2018) Espnet: efficient spatial pyramid of dilated convolutions for semantic segmentation. In: Proceedings of the European conference on computer vision (ECCV) 552\u2013568","DOI":"10.1007\/978-3-030-01249-6_34"},{"key":"1610_CR55","doi-asserted-by":"crossref","unstructured":"Mehta S, Rastegari M, Shapiro L, Hajishirzi H (2019) Espnetv2: a light-weight, power efficient, and general purpose convolutional neural network. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition 9190\u20139200","DOI":"10.1109\/CVPR.2019.00941"},{"key":"1610_CR56","doi-asserted-by":"crossref","unstructured":"Liu Z, Mao H, Wu C-Y, Feichtenhofer C, Darrell T, Xie S (2022) A convnet for the 2020s. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition 11976\u201311986","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"1610_CR57","doi-asserted-by":"crossref","unstructured":"Liu X, Peng H, Zheng N, Yang Y, Hu H, Yuan Y (2023) Efficientvit: memory efficient vision transformer with cascaded group attention. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition 14420\u201314430","DOI":"10.1109\/CVPR52729.2023.01386"},{"key":"1610_CR58","unstructured":"Wang Z, Li C, Xu H, Zhu X (2024) Mamba yolo: Ssms-based yolo for object detection. arXiv preprint arXiv:2406.05835,"},{"key":"1610_CR59","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 10781\u201310790","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"1610_CR60","doi-asserted-by":"publisher","first-page":"141861","DOI":"10.1109\/ACCESS.2021.3120870","volume":"9","author":"S Li","year":"2021","unstructured":"Li S, Li Y, Li Y, Li M, Xu X (2021) Yolo-firi: improved yolov5 for infrared image object detection. IEEE Access 9:141861\u2013141875","journal-title":"IEEE Access"},{"key":"1610_CR61","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":"1610_CR62","doi-asserted-by":"crossref","unstructured":"Wang C-Y, Yeh I-H, Mark Liao H-Y (2024) Yolov9: learning what you want to learn using programmable gradient information. In: European conference on computer vision. Springer 1\u201321","DOI":"10.1007\/978-3-031-72751-1_1"}],"container-title":["Pattern Analysis and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-026-01610-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10044-026-01610-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-026-01610-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T10:38:40Z","timestamp":1773484720000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10044-026-01610-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,17]]},"references-count":62,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["1610"],"URL":"https:\/\/doi.org\/10.1007\/s10044-026-01610-5","relation":{},"ISSN":["1433-7541","1433-755X"],"issn-type":[{"value":"1433-7541","type":"print"},{"value":"1433-755X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,17]]},"assertion":[{"value":"25 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 January 2026","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":"This work does not include studies on human subjects, human data or tissue, or animals.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"29"}}