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<p>With the rapid expansion of the low-altitude economy, unmanned aerial vehicles (UAVs) equipped with vision systems have become essential platforms for various aerial perception tasks; nevertheless, optimizing the accuracy-efficiency trade-off for small object detection in aerial imagery remains a major challenge. To address this challenge, we propose a new real-time detector for aerial imagery based on YOLO11, named RSS-YOLO. First, a Reciprocal Interaction Fusion (RIF) module is designed to replace the C3k2 module in YOLO11. This module enables complementary information exchange across multiple feature levels via bidirectional feature interaction. By preserving spatial localization details while refining semantic cues, it alleviates insufficient integration of spatial and semantic information within the feature extraction layers. The neck architecture is further optimized by introducing a P2 small object detection layer integrating an improved SCSA attention mechanism, while removing the redundant P5 detection layer. Additionally, replacing standard convolutions with the L-Down module in deep networks strips away redundant parameters and computation without sacrificing performance. Experimental results on the VisDrone2019 dataset demonstrate that the proposed method improves mAP @0.5 and mAP @0.5:0.95 by 3.8% and 3.0%, respectively, compared with the baseline YOLO11 model, while reducing the parameter count by 48.06%.</p>

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RSS-YOLO: An efficient UAV object detector with reciprocal spatial-semantic fusion

  • 期刊论文

  • 发表日期：

    2026年7月16日

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  • Pengfei Dai,
  • Liang Chen,
  • Le Xie,
  • Longhua Zhou

摘要

With the rapid expansion of the low-altitude economy, unmanned aerial vehicles (UAVs) equipped with vision systems have become essential platforms for various aerial perception tasks; nevertheless, optimizing the accuracy-efficiency trade-off for small object detection in aerial imagery remains a major challenge. To address this challenge, we propose a new real-time detector for aerial imagery based on YOLO11, named RSS-YOLO. First, a Reciprocal Interaction Fusion (RIF) module is designed to replace the C3k2 module in YOLO11. This module enables complementary information exchange across multiple feature levels via bidirectional feature interaction. By preserving spatial localization details while refining semantic cues, it alleviates insufficient integration of spatial and semantic information within the feature extraction layers. The neck architecture is further optimized by introducing a P2 small object detection layer integrating an improved SCSA attention mechanism, while removing the redundant P5 detection layer. Additionally, replacing standard convolutions with the L-Down module in deep networks strips away redundant parameters and computation without sacrificing performance. Experimental results on the VisDrone2019 dataset demonstrate that the proposed method improves mAP @0.5 and mAP @0.5:0.95 by 3.8% and 3.0%, respectively, compared with the baseline YOLO11 model, while reducing the parameter count by 48.06%.