  Remote sensor image object detection is an important technology for Earth
observation, and is used in various tasks such as forest fire monitoring and
ocean monitoring. Image object detection technology, despite the significant
developments, is struggling to handle remote sensor images and small-scale
objects, due to the limited pixels of small objects. Numerous existing studies
have demonstrated that an effective way to promote small object detection is to
introduce the spatial context. Meanwhile, recent researches for image
classification have shown that spectral convolution operations can perceive
long-term spatial dependence more efficiently in the frequency domain than
spatial domain. Inspired by this observation, we propose a Frequency-aware
Feature Pyramid Framework (FFPF) for remote sensing object detection, which
consists of a novel Frequency-aware ResNet (F-ResNet) and a Bilateral
Spectral-aware Feature Pyramid Network (BS-FPN). Specifically, the F-ResNet is
proposed to perceive the spectral context information by plugging the frequency
domain convolution into each stage of the backbone, extracting richer features
of small objects. To the best of our knowledge, this is the first work to
introduce frequency-domain convolution into remote sensing object detection
task. In addition, the BSFPN is designed to use a bilateral sampling strategy
and skipping connection to better model the association of object features at
different scales, towards unleashing the potential of the spectral context
information from F-ResNet. Extensive experiments are conducted for object
detection in the optical remote sensing image dataset (DIOR and DOTA). The
experimental results demonstrate the excellent performance of our method. It
achieves an average accuracy (mAP) without any tricks.
