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Detection and tracking of small objects like vehicles, aeroplanes and ships from satellite imagery targets several critical applications in urban planning, air and sea traffic management, marine surveillance, disaster prediction and relief, etc. But accurately locating small-objects in large and complex satellite images is highly challenging due to loss of contextual information, blurred objects, diminished sizes, etc. This paper proposes a novel dual attention-integrated YOLO model to accurately detect small-objects in the stand Skyfusion dataset. The selected baseline YOLOv11s is fused with global attention and Efficient channel attention mechanisms at varying feature resolutions for retaining global contextual and spatial information along with channel inter-dependencies. The proposed work applies targeted data augmentation to oversample the Ship class instances and benchmarks the proposed technique against state-of-the-art models with the highest F-score and mAP of \(72.4\%\) and \(71.3\%\) . With minimal increase in parameters, the proposed model thus serves as a scalable and reliable solution for tiny object detection in challenging aerial imagery.

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Leveraging Small-Object Detection in Satellite Imagery Using Dual Attention-Augmented Single-Stage CNN Model

  • 会议论文

  • 发表日期：

    2026年

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  • Likhit Yammanuru,
  • Nikhil Tom Jose,
  • Rimjhim Padam Singh

摘要

Detection and tracking of small objects like vehicles, aeroplanes and ships from satellite imagery targets several critical applications in urban planning, air and sea traffic management, marine surveillance, disaster prediction and relief, etc. But accurately locating small-objects in large and complex satellite images is highly challenging due to loss of contextual information, blurred objects, diminished sizes, etc. This paper proposes a novel dual attention-integrated YOLO model to accurately detect small-objects in the stand Skyfusion dataset. The selected baseline YOLOv11s is fused with global attention and Efficient channel attention mechanisms at varying feature resolutions for retaining global contextual and spatial information along with channel inter-dependencies. The proposed work applies targeted data augmentation to oversample the Ship class instances and benchmarks the proposed technique against state-of-the-art models with the highest F-score and mAP of \(72.4\%\) and \(71.3\%\) . With minimal increase in parameters, the proposed model thus serves as a scalable and reliable solution for tiny object detection in challenging aerial imagery.