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By using shared large kernels and hierarchical dynamic kernels, CESC emulates long-range dependency modeling and instance-adaptive behaviors of self-attention, strengthening multi-scale representations while reducing computational cost. Second, we introduce the Linear Patch-Aware Attention Feature Interaction (LPAFI) module. A multi-branch patch-aware attention mechanism captures cross-scale local correspondences, and a multi-scale linear attention mechanism further fuses CNN-style local detail representation with global context modeling, enabling efficient integration of local and global features. Finally, we propose Matchability-Varifocal Loss (MVL) to better optimize low-quality matched samples. MVL combines IoU-aware and matchability-aware weighting to dynamically adjust loss contributions and strengthen supervision for low-quality samples, accelerating convergence and improving robustness. Experiments on VisDrone2019 show that CSMA-DETR significantly outperforms the baseline, improving mAP50 and mAP50:95 by 5% and 2%, respectively, while reducing parameters and computation by 7.0% and 4.6%. Additional evaluations on DIOR and HIT-UAV further verify its effectiveness in cluttered scenes with densely distributed small objects, providing an efficient solution for real-time UAV detection.<\/jats:p>","DOI":"10.1007\/s44443-026-00621-w","type":"journal-article","created":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T18:04:56Z","timestamp":1774116296000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Efficient transformer-based UAV image object detection network based on convolutional self-attention and matchability-adaptive classification"],"prefix":"10.1007","volume":"38","author":[{"given":"Juxing","family":"Di","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,21]]},"reference":[{"key":"621_CR1","doi-asserted-by":"crossref","unstructured":"Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S (2020) End-to-end object detection with transformers. 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