BDRNet: Background-Aware Dynamic-Scale Routing Network for UAV Remote Sensing Object Detection.
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| Title: | BDRNet: Background-Aware Dynamic-Scale Routing Network for UAV Remote Sensing Object Detection. |
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| Authors: | Zheng, Xuelong1 (AUTHOR), Shao, Faming1 (AUTHOR), Liu, Qing1 (AUTHOR) liuqing4942@126.com, Dai, Juying1 (AUTHOR), Yue, Yiming1 (AUTHOR), Zhang, Tao1 (AUTHOR), Chen, Caian1 (AUTHOR) |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p1987. 26p. |
| Subjects: | Remote sensing, Object recognition (Computer vision), Loss functions (Statistics), Clutter (Radar) |
| Abstract: | Highlights: What are the main findings? UAV aerial imagery suffers from three persistent bottlenecks—complex background clutter, drastic scale variation, and gradient instability for extremely small objects—that existing lightweight detectors fail to address simultaneously. Standard attention mechanisms enhance foreground indiscriminately, static FPN fusion cannot adapt to content, and IoU-based losses are overly sensitive to small object offsets, collectively limiting detection accuracy in UAV scenarios. What are the implications of the main findings? The consistent performance gains across two diverse UAV benchmarks validate that jointly optimizing feature suppression, multi-scale fusion, and regression objectives is a promising direction for advancing small object detection in complex remote sensing scenarios. The proposed approach has broad implications for remote sensing applications requiring real-time aerial perception, including urban traffic monitoring, infrastructure inspection, disaster assessment, and precision agriculture. Object detection in UAV remote sensing imagery remains challenging due to severe scale variation, dense object distributions, complex background clutter, and localization ambiguity caused by extremely small objects. To address these issues, this paper proposes BDRNet, a lightweight background-aware dynamic-scale routing network for UAV remote sensing object detection. First, a background-aware feature enhancement (BAFE) module is introduced into the backbone to enhance feature representation through horizontal and vertical contextual modeling, improving target-related responses in complex aerial scenes. Second, a dynamic-scale routing pyramid (DSRP) is designed to retain the high-resolution P 2 branch and adaptively integrate multi-scale features through spatially dynamic routing, alleviating the loss of fine-grained information and improving the representation of small and scale-varied objects. Third, a scale- and geometry-aware normalized Wasserstein distance (SGNW) loss is proposed by modeling bounding boxes as two-dimensional Gaussian distributions. By incorporating aspect-ratio-guided geometric weighting and scale-aware dynamic fusion, SGNW improves regression stability for small objects while preserving geometric constraints for medium and large targets. Extensive experiments on the VisDrone2019 and UAVDT datasets demonstrate that BDRNet consistently improves detection accuracy over the YOLOv10s detector while maintaining a comparable model size and computational cost. Compared with several mainstream lightweight detectors, BDRNet achieves a favorable accuracy–efficiency trade-off, demonstrating its effectiveness for UAV remote sensing object detection in complex aerial scenarios. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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