Detecting interested targets on aerial robotic systems is a challenging task. Due to the long view distance of air-to-ground observation, the target size is small and the number is large in the scene. In addition, the target only occupies part of the image, and the complex background environment can easily cover the feature information of the target. In this paper, a novel target detection method based on spatial attention model is designed, which changes the existing methods to enhance the features of target areas by enhancing global semantic information. By learning the feature weights of different spatial locations in feature space, the method proposed can focus attention on the target regions of interest in an image, and suppress the background interference features, which enhances the feature information of the target regions, and deals with the class imbalance problem in detection. The experimental results show that the algorithm improves the detection accuracy of small air-to-ground targets and has a good detection effect for dense target areas. Compared with RefineDet, the state-of-art small target detector, our method can achieve better performance at a lower cost.

中文翻译：

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基于空间注意力模型的航空机器人系统目标检测

在空中机器人系统上检测感兴趣的目标是一项艰巨的任务。由于空对地观察的长视距离，因此目标尺寸小且场景中的数量大。另外，目标仅占据图像的一部分，复杂的背景环境可以轻松覆盖目标的特征信息。本文设计了一种基于空间注意力模型的目标检测方法，该方法改变了现有方法，通过增强全局语义信息来增强目标区域的特征。通过学习特征空间中不同空间位置的特征权重，提出的方法可以将注意力集中在图像中的目标目标区域上，并抑制背景干扰特征，从而增强了目标区域的特征信息；并处理检测中的类不平衡问题。实验结果表明，该算法提高了小空对地目标的检测精度，对密集的目标区域具有良好的检测效果。与最先进的小型目标检测器RefineDet相比，我们的方法可以以更低的成本获得更好的性能。