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This paper presents an enhanced YOLOv11 model integrated with BiFPN and GLSA attention mechanisms to advance geospatial target recognition. BiFPN enables efficient bidirectional multi-scale feature fusion, while GLSA balances global context and local detail extraction, addressing challenges like subtle target features, background interference, and small object detection. Evaluations on the COCO dataset and a self-built dataset encompassing 6 urban road target categories demonstrate the improved model outperforms YOLOv8, Faster R-CNN, and the original YOLOv11. On the self-built dataset, it achieves 57.4% mAP50, 33.2% mAP50–95, and 74.5 FPS. Experimental results validate its significant real-time performance and robustness in geospatial scenarios, providing technical support for resource exploration and environmental monitoring.

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Geospatial Target Recognition Using Feature-Enhanced YOLOv11

  • 会议论文

  • 发表日期：

    2026年

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  • Lizhi Li,
  • Mei Wang,
  • Junjie Shi,
  • Xinyan Li,
  • Fanggang Fu,
  • Lukang Yao

摘要

This paper presents an enhanced YOLOv11 model integrated with BiFPN and GLSA attention mechanisms to advance geospatial target recognition. BiFPN enables efficient bidirectional multi-scale feature fusion, while GLSA balances global context and local detail extraction, addressing challenges like subtle target features, background interference, and small object detection. Evaluations on the COCO dataset and a self-built dataset encompassing 6 urban road target categories demonstrate the improved model outperforms YOLOv8, Faster R-CNN, and the original YOLOv11. On the self-built dataset, it achieves 57.4% mAP50, 33.2% mAP50–95, and 74.5 FPS. Experimental results validate its significant real-time performance and robustness in geospatial scenarios, providing technical support for resource exploration and environmental monitoring.