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To enhance multi-scale feature representation and detection efficiency, this paper proposes MSEF-YOLO11s. Specifically, we first design a lightweight partial multi-scale (LPMS) module, which effectively aggregates cross-scale information and enhances multi-scale representations in the backbone for small objects. Secondly, to dynamically adjust feature weights and mitigate feature conflicts in the neck, we devise a multi-scale boundary-semantic alignment (MS-BSA) based on adaptive attention, which can further avoid computational redundancy for sufficient fusion. Finally, a lightweight shared detail detection head (LSDDH) replaces the decoupled head structure with shared convolutional layers, resolving the issue of parameter explosion associated with adding a dedicated small object detection head. Experimental results demonstrate the effectiveness of the proposed model. Specifically, compared to the baseline YOLO11s, MSEF-YOLO11s achieves an improvement of 6.6% in mAP50 on the VisDrone2019 test set, with only 4.4M increase in parameters. Furthermore, mAP50 on the TinyPerson test set increases from 22.8% to 28.1%, confirming the model\u2019s strong generalization capability.<\/jats:p>","DOI":"10.1007\/s11227-025-08186-7","type":"journal-article","created":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T01:51:37Z","timestamp":1768873897000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MSEF-YOLO11s: a multi-scale extraction and fusion network for small target detection in drone imagery"],"prefix":"10.1007","volume":"82","author":[{"given":"Kai","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Pengcheng","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Farhan","family":"Ullah","sequence":"additional","affiliation":[]},{"given":"Yue","family":"Zhao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,20]]},"reference":[{"key":"8186_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2023.103670","volume":"216","author":"K Messaoudi","year":"2023","unstructured":"Messaoudi K, Oubbati OS, Rachedi A, Lakas A, Bendouma T, Chaib N (2023) A survey of UAV-based data collection: challenges, solutions and future perspectives. 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