UFO-DETR: Frequency-Guided End-to-End Detector for UAV Tiny Objects
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866914352608575488 |
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| author | Chen, Yuankai Lin, Kai Wu, Qihong Yang, Xinxuan Lai, Jiashuo Chen, Ruoen Shi, Haonan He, Minfan Wang, Meihua |
| author_facet | Chen, Yuankai Lin, Kai Wu, Qihong Yang, Xinxuan Lai, Jiashuo Chen, Ruoen Shi, Haonan He, Minfan Wang, Meihua |
| contents | Small target detection in UAV imagery faces significant challenges such as scale variations, dense distribution, and the dominance of small targets. Existing algorithms rely on manually designed components, and general-purpose detectors are not optimized for UAV images, making it difficult to balance accuracy and complexity. To address these challenges, this paper proposes an end-to-end object detection framework, UFO-DETR, which integrates an LSKNet-based backbone network to optimize the receptive field and reduce the number of parameters. By combining the DAttention and AIFI modules, the model flexibly models multi-scale spatial relationships, improving multi-scale target detection performance. Additionally, the DynFreq-C3 module is proposed to enhance small target detection capability through cross-space frequency feature enhancement. Experimental results show that, compared to RT-DETR-L, the proposed method offers significant advantages in both detection performance and computational efficiency, providing an efficient solution for UAV edge computing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_22712 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | UFO-DETR: Frequency-Guided End-to-End Detector for UAV Tiny Objects Chen, Yuankai Lin, Kai Wu, Qihong Yang, Xinxuan Lai, Jiashuo Chen, Ruoen Shi, Haonan He, Minfan Wang, Meihua Computer Vision and Pattern Recognition Small target detection in UAV imagery faces significant challenges such as scale variations, dense distribution, and the dominance of small targets. Existing algorithms rely on manually designed components, and general-purpose detectors are not optimized for UAV images, making it difficult to balance accuracy and complexity. To address these challenges, this paper proposes an end-to-end object detection framework, UFO-DETR, which integrates an LSKNet-based backbone network to optimize the receptive field and reduce the number of parameters. By combining the DAttention and AIFI modules, the model flexibly models multi-scale spatial relationships, improving multi-scale target detection performance. Additionally, the DynFreq-C3 module is proposed to enhance small target detection capability through cross-space frequency feature enhancement. Experimental results show that, compared to RT-DETR-L, the proposed method offers significant advantages in both detection performance and computational efficiency, providing an efficient solution for UAV edge computing. |
| title | UFO-DETR: Frequency-Guided End-to-End Detector for UAV Tiny Objects |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.22712 |