  Taking the deep learning-based algorithms into account has become a crucial
way to boost object detection performance in aerial images. While various
neural network representations have been developed, previous works are still
inefficient to investigate the noise-resilient performance, especially on
aerial images with noise taken by the cameras with telephoto lenses, and most
of the research is concentrated in the field of denoising. Of course, denoising
usually requires an additional computational burden to obtain higher quality
images, while noise-resilient is more of a description of the robustness of the
network itself to different noises, which is an attribute of the algorithm
itself. For this reason, the work will be started by analyzing the
noise-resilient performance of the neural network, and then propose two
hypotheses to build a noise-resilient structure. Based on these hypotheses, we
compare the noise-resilient ability of the Oct-ResNet with frequency division
processing and the commonly used ResNet. In addition, previous feature pyramid
networks used for aerial object detection tasks are not specifically designed
for the frequency division feature maps of the Oct-ResNet, and they usually
lack attention to bridging the semantic gap between diverse feature maps from
different depths. On the basis of this, a novel octave convolution-based
semantic attention feature pyramid network (OcSaFPN) is proposed to get higher
accuracy in object detection with noise. The proposed algorithm tested on three
datasets demonstrates that the proposed OcSaFPN achieves a state-of-the-art
detection performance with Gaussian noise or multiplicative noise. In addition,
more experiments have proved that the OcSaFPN structure can be easily added to
existing algorithms, and the noise-resilient ability can be effectively
improved.
