{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T21:19:52Z","timestamp":1776979192158,"version":"3.51.4"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"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":["SIViP"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s11760-026-05291-9","type":"journal-article","created":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T09:23:22Z","timestamp":1775035402000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-stage feature fusion network for UAV aerial object detection"],"prefix":"10.1007","volume":"20","author":[{"given":"Xueqiang","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,1]]},"reference":[{"key":"5291_CR1","doi-asserted-by":"publisher","first-page":"24835","DOI":"10.1109\/ACCESS.2023.3255164","volume":"11","author":"F Viel","year":"2023","unstructured":"Viel, F., Maciel, R.C., Seman, L.O., et al.: Hyperspectral image classification: An analysis employing cnn, lstm, transformer, and attention mechanism. IEEE Access 11, 24835\u201324850 (2023)","journal-title":"IEEE Access"},{"issue":"6","key":"5291_CR2","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., et al.: Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"5291_CR3","doi-asserted-by":"publisher","first-page":"65496","DOI":"10.1109\/ACCESS.2022.3183604","volume":"10","author":"C Loraksa","year":"2022","unstructured":"Loraksa, C., Mongkolsomlit, S., Nimsuk, N., et al.: Development of the osteosarcoma lung nodules detection model based on ssd-vgg16 and competency comparing with traditional method. IEEE Access 10, 65496\u201365506 (2022)","journal-title":"IEEE Access"},{"key":"5291_CR4","doi-asserted-by":"crossref","unstructured":"W.\u00a0Liu, D.\u00a0Anguelov, D.\u00a0Erhan, et\u00a0al., \u201cSsd: Single shot multibox detector,\u201d in Computer Vision \u2013 ECCV 2016, B.\u00a0Leibe, J.\u00a0Matas, N.\u00a0Sebe, et\u00a0al., Eds., 21\u201337, Springer International Publishing, (Cham) (2016)","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"5291_CR5","doi-asserted-by":"crossref","unstructured":"K.\u00a0Duan, S.\u00a0Bai, L.\u00a0Xie, et\u00a0al., \u201cCenternet: Keypoint triplets for object detection,\u201d in 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), 6568\u20136577 (2019)","DOI":"10.1109\/ICCV.2019.00667"},{"key":"5291_CR6","doi-asserted-by":"crossref","unstructured":"D.\u00a0Padilla\u00a0Carrasco, H.\u00a0A. Rashwan, M.\u00a0\u00c2. Garc\u00c2\u00a8\u00c2\u00aaa, et\u00a0al., \u201cT-yolo: Tiny vehicle detection based on yolo and multi-scale convolutional neural networks,\u201d IEEE Access 11, 22430\u201322440 (2023)","DOI":"10.1109\/ACCESS.2021.3137638"},{"key":"5291_CR7","doi-asserted-by":"crossref","unstructured":"J.\u00a0Redmon, S.\u00a0Divvala, R.\u00a0Girshick, et\u00a0al., \u201cYou only look once: Unified, real-time object detection,\u201d in Computer Vision and Pattern Recognition, (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"5291_CR8","doi-asserted-by":"crossref","unstructured":"Z.\u00a0Cai and N.\u00a0Vasconcelos, \u201cCascade r-cnn: Delving into high quality object detection,\u201d in 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 6154\u20136162 (2018)","DOI":"10.1109\/CVPR.2018.00644"},{"key":"5291_CR9","doi-asserted-by":"crossref","unstructured":"A.\u00a0Meethal, E.\u00a0Granger, and M.\u00a0Pedersoli, \u201cCascaded zoom-in detector for high resolution aerial images,\u201d in 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2046\u20132055 (2023)","DOI":"10.1109\/CVPRW59228.2023.00198"},{"key":"5291_CR10","doi-asserted-by":"crossref","unstructured":"T.-Y. Lin, P.\u00a0Dollr, R.\u00a0Girshick, et\u00a0al., \u201cFeature pyramid networks for object detection,\u201d in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 936\u2013944 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"5291_CR11","doi-asserted-by":"crossref","unstructured":"C.\u00a0Chen, Y.\u00a0Zhang, Q.\u00a0Lv, et\u00a0al., \u201cRrnet: A hybrid detector for object detection in drone-captured images,\u201d in 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW), 100\u2013108 (2019)","DOI":"10.1109\/ICCVW.2019.00018"},{"key":"5291_CR12","doi-asserted-by":"crossref","unstructured":"S.\u00a0Zhang, L.\u00a0Wen, X.\u00a0Bian, et\u00a0al., \u201cSingle-shot refinement neural network for object detection,\u201d in 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 4203\u20134212 (2018)","DOI":"10.1109\/CVPR.2018.00442"},{"key":"5291_CR13","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.jvcir.2015.11.002","volume":"34","author":"S Razakarivony","year":"2016","unstructured":"Razakarivony, S., Jurie, F.: Vehicle detection in aerial imagery: A small target detection benchmark. J. Vis. Commun. Image Represent. 34, 187\u2013203 (2016)","journal-title":"J. Vis. Commun. Image Represent."},{"key":"5291_CR14","doi-asserted-by":"crossref","unstructured":"H.\u00a0Zhu, X.\u00a0Chen, W.\u00a0Dai, et\u00a0al., \u201cOrientation robust object detection in aerial images using deep convolutional neural network,\u201d in 2015 IEEE International Conference on Image Processing (ICIP), 3735\u20133739 (2015)","DOI":"10.1109\/ICIP.2015.7351502"},{"issue":"11","key":"5291_CR15","doi-asserted-by":"publisher","first-page":"7380","DOI":"10.1109\/TPAMI.2021.3119563","volume":"44","author":"P Zhu","year":"2022","unstructured":"Zhu, P., Wen, L., Du, D., et al.: Detection and tracking meet drones challenge. IEEE Trans. Pattern Anal. Mach. Intell. 44(11), 7380\u20137399 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"5291_CR16","unstructured":"J.\u00a0Redmon and A.\u00a0Farhadi, \u201cYolov3: An incremental improvement,\u201d ArXiv abs\/1804.02767 (2018)"},{"key":"5291_CR17","unstructured":"A.\u00a0Bochkovskiy, C.-Y. Wang, and H.-Y.\u00a0M. Liao, \u201cYolov4: Optimal speed and accuracy of object detection,\u201d ArXiv abs\/2004.10934 (2020)"},{"key":"5291_CR18","doi-asserted-by":"crossref","unstructured":"C.-Y. Wang, H.-Y. Mark\u00a0Liao, Y.-H. Wu, et\u00a0al., \u201cCspnet: A new backbone that can enhance learning capability of cnn,\u201d in 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 1571\u20131580 (2020)","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"5291_CR19","unstructured":"G.\u00a0R. Jocher, A.\u00a0Stoken, J.Borovec, et\u00a0al., \u201cultralytics\/yolov5: v3.1 - bug fixes and performance improvements,\u201d (2020)"},{"key":"5291_CR20","doi-asserted-by":"crossref","unstructured":"C.-Y. Wang, A.\u00a0Bochkovskiy, and H.-Y.\u00a0M. Liao, \u201cYolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,\u201d in 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 7464\u20137475 (2023)","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"5291_CR21","unstructured":"G.\u00a0Jocher, A.\u00a0Chaurasia, and J.\u00a0Qiu, \u201cUltralytics yolov8,\u201d (2023)"},{"key":"5291_CR22","doi-asserted-by":"crossref","unstructured":"C.-Y. Wang, I.-H. Yeh, and H.\u00a0Liao, \u201cYolov9: Learning what you want to learn using programmable gradient information,\u201d ArXiv abs\/2402.13616 (2024)","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"5291_CR23","unstructured":"A.\u00a0Wang, H.\u00a0Chen, L.\u00a0Liu, et\u00a0al., \u201cYolov10: Real-time end-to-end object detection,\u201d ArXiv abs\/2405.14458 (2024)"},{"key":"5291_CR24","doi-asserted-by":"crossref","unstructured":"Y. Zhao, W.\u00a0Lv, S.\u00a0Xu, et\u00a0al., \u201cDetrs beat yolos on real-time object detection,\u201d 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 16965\u201316974 (2023)","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"5291_CR25","doi-asserted-by":"crossref","unstructured":"S.\u00a0Woo, J.\u00a0Park, J.-Y. Lee, et\u00a0al., \u201cCbam: Convolutional block attention module,\u201d in Computer Vision \u2013 ECCV 2018, V.\u00a0Ferrari, M.\u00a0Hebert, C.\u00a0Sminchisescu, et\u00a0al., Eds., 3\u201319, Springer International Publishing, (Cham) (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"5291_CR26","doi-asserted-by":"crossref","unstructured":"J.\u00a0Hu, L.\u00a0Shen, and G.\u00a0Sun, \u201cSqueeze-and-excitation networks,\u201d in 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"5291_CR27","doi-asserted-by":"crossref","unstructured":"H.\u00a0Xu, Y.\u00a0Xu, and K.\u00a0Hu, \u201cA vision-based inspection system for pharmaceutical production line,\u201d The Journal of Supercomputing 81(625) (2025)","DOI":"10.1007\/s11227-025-07135-8"},{"key":"5291_CR28","doi-asserted-by":"crossref","unstructured":"H.\u00a0Xu, Q.\u00a0Liu, J.\u00a0Zhu, et\u00a0al., \u201cCslnet: An enhanced yolov8-based approach to defect surface foreign objects in lyophilized powder,\u201d Signal, Image and Video Processing 19(728) (2025)","DOI":"10.1007\/s11760-025-04335-w"},{"key":"5291_CR29","doi-asserted-by":"crossref","unstructured":"S.\u00a0Liu, L.\u00a0Qi, H.\u00a0Qin, et\u00a0al., \u201cPath aggregation network for instance segmentation,\u201d in 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 8759\u20138768 (2018)","DOI":"10.1109\/CVPR.2018.00913"},{"issue":"4","key":"5291_CR30","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"L-C Chen","year":"2018","unstructured":"Chen, L.-C., Papandreou, G., Kokkinos, I., et al.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834\u2013848 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"5291_CR31","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1049\/cvi2.12161","volume":"17","author":"J Yuan","year":"2022","unstructured":"Yuan, J., Hu, Y., Sun, Y., et al.: A multi-scale feature representation and interaction network for underwater object detection. IET Comput. Vis. 17, 265\u2013281 (2022)","journal-title":"IET Comput. Vis."},{"key":"5291_CR32","doi-asserted-by":"crossref","unstructured":"Z. Shao, Y.\u00a0Yin, H.\u00a0Lyu, et\u00a0al., \u201cAn efficient model for small object detection in the maritime environment,\u201d Applied Ocean Research (2024)","DOI":"10.1016\/j.apor.2024.104194"},{"key":"5291_CR33","first-page":"13467","volume":"45","author":"G Cheng","year":"2023","unstructured":"Cheng, G., Yuan, X., Yao, X., et al.: Towards large-scale small object detection: Survey and benchmarks. IEEE Trans. Pattern Anal. Mach. Intell. 45, 13467\u201313488 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-026-05291-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-026-05291-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-026-05291-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T20:32:27Z","timestamp":1776976347000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-026-05291-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":33,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["5291"],"URL":"https:\/\/doi.org\/10.1007\/s11760-026-05291-9","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"28 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 July 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 March 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 April 2026","order":4,"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 competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"238"}}