论文标题
Reffpn：用于空中对象检测的旋转等级注意融合金字塔网络
ReAFFPN: Rotation-equivariant Attention Feature Fusion Pyramid Networks for Aerial Object Detection
论文作者
论文摘要
本文提出了用于空中对象检测的旋转等值融合融合金字塔网络，称为Reffpn。 Reffpn的目的是改善旋转等值的特征在相邻层之间融合的效果，该层受到语义和尺度不连续性的影响。由于旋转模棱两可的卷积的特殊性，一般方法无法在确保网络旋转等同于网络的同时达到其原始效果。为了解决这个问题，我们设计了一种新的旋转等值通道的注意力，该通道的注意力既可以引起通道注意力并保持旋转均等。然后，我们将新的通道注意力函数嵌入到迭代的注意特征融合（IAFF）模块中，以实现旋转等级的注意特征融合。实验结果表明，ReffPN具有更好的旋转等值功能融合能力，并显着提高了旋转等级卷积网络的准确性。
This paper proposes a Rotation-equivariant Attention Feature Fusion Pyramid Networks for Aerial Object Detection named ReAFFPN. ReAFFPN aims at improving the effect of rotation-equivariant features fusion between adjacent layers which suffers from the semantic and scale discontinuity. Due to the particularity of rotational equivariant convolution, general methods are unable to achieve their original effect while ensuring rotation equivariance of the network. To solve this problem, we design a new Rotation-equivariant Channel Attention which has the ability to both generate channel attention and keep rotation equivariance. Then we embed a new channel attention function into Iterative Attentional Feature Fusion (iAFF) module to realize Rotation-equivariant Attention Feature Fusion. Experimental results demonstrate that ReAFFPN achieves a better rotation-equivariant feature fusion ability and significantly improve the accuracy of the Rotation-equivariant Convolutional Networks.
