Compared with the classic object detection problem, detecting objects in aerial images has some special challenges including huge orientation variations, complicated and large background, and wide multi-scale distribution. Considering these three challenges together, we propose a novel arbitrary-oriented object detection framework consisting of three main parts. Firstly, the Cascading Attention Network (CA-Net) composed of a patching self-attention module and a supervised spatial attention module is proposed for enhancing the feature representations from objects of interest and suppressing the background noises in Feature Pyramid Network (FPN) from coarse to fine. Then, the Adaptive Feature Concatenate Network (AFC-Net) is proposed to adaptively stack the feature maps pooled from all FPN levels as well as the global semantic features, for dealing with the multi-scale change of objects. Lastly, the OBB Multi-Definition and Selection Strategy (OBB-MDS-Strategy) is proposed to regress rotated bounding boxes more smoothly and detect oriented objects more accurately in the training process. Our experiments are conducted on two common and challenging aerial datasets, i.e., DOTA and HRSC2016. Experiments results show that the proposed method has superior performances in multi-orientated objects detection compared with the representative methods.

与经典的物体检测问题相比，航空图像中的物体检测面临着一些特殊的挑战，包括巨大的方向变化，复杂而庞大的背景以及广泛的多尺度分布。综合考虑这三个挑战，我们提出了一个新颖的面向任意对象的检测框架，该框架由三个主要部分组成。首先，提出了一种由补丁自注意力模块和有监督的空间注意力模块组成的级联注意力网络（CA-Net），用于增强感兴趣对象的特征表示，并抑制特征金字塔网络（FPN）中的背景噪声。罚款。然后，自适应特征级联网络提出（AFC-Net）自适应地堆叠从所有FPN级别汇集的特征图以及全局语义特征，以处理对象的多尺度变化。最后，提出了OBB多定义和选择策略（OBB-MDS-Strategy），以在训练过程中更平滑地回归旋转的边界框并更准确地检测定向对象。我们的实验是在两个常见且具有挑战性的航空数据集上进行的，即DOTA和HRSC2016。实验结果表明，与典型方法相比，该方法在多向目标检测中具有更好的性能。