Drone object detection in low-altitude airspace plays an essential role in many practical applications, such as security and airspace monitoring. Despite the remarkable progress made by many methods, drone object detection still remains challenging due to the complex background and huge differences in scales of drones. To address the above issues, an improved fully convolutional one-stage object detection (FCOS) model based on adaptive weighted feature fusion (AWFF) module is proposed for multiscale drone object detection in complex background. By learning the spatial relevance of feature maps at each scale and improving the scale invariance of features based on the channel attention mechanism, AWFF module could adaptively fuse the features of adjacent scale. In addition, a receptive field enhancement module is designed to reduce the information loss in the feature fusion process. Extensive experiments are conducted to evaluate the effectiveness of the proposed module and method on the constructed low-altitude drone dataset, which concludes that the mean average precision of the AWFF-FCOS is increased by 2.1% compared with the baseline method. And extensive ablation experiments further demonstrate that the proposed AWFF module and REF module could be integrated into the state-of-the-art method to improve the performance of drone object detection.

中文翻译：

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用于小型无人机目标检测的自适应加权多尺度特征融合

低空空域无人机目标检测在许多实际应用中发挥着至关重要的作用，例如安全和空域监控。尽管许多方法取得了显着进展，但由于无人机背景复杂且规模差异巨大，无人机目标检测仍然具有挑战性。针对上述问题，提出了一种基于自适应加权特征融合（AWFF）模块的改进型全卷积单阶段目标检测（FCOS）模型，用于复杂背景下的多尺度无人机目标检测。AWFF模块通过学习各尺度特征图的空间相关性，基于通道注意力机制提高特征尺度不变性，自适应融合相邻尺度特征。此外，设计了一个感受野增强模块来减少特征融合过程中的信息丢失。进行了广泛的实验以评估所提出的模块和方法在构建的低空无人机数据集上的有效性，得出的结论是，与基线方法相比，AWFF-FCOS 的平均精度提高了 2.1%。大量的消融实验进一步表明，所提出的 AWFF 模块和 REF 模块可以集成到最先进的方法中，以提高无人机目标检测的性能。与基线方法相比 1%。大量的消融实验进一步表明，所提出的 AWFF 模块和 REF 模块可以集成到最先进的方法中，以提高无人机目标检测的性能。与基线方法相比 1%。大量的消融实验进一步表明，所提出的 AWFF 模块和 REF 模块可以集成到最先进的方法中，以提高无人机目标检测的性能。