Spectral Network Combining Fourier Transformation and Deep Learning for Remote Sensing Object Detection

While general object detection with deep learning techniques has garnered immense progress, the performance of detecting small objects in remote sensing is far from satisfactory due to the lack of sufficient details. To address this problem, this paper designs a spectral network combining Fast Fourier Convolution (FFC) and detection network. By extending the receptive region of the network, features around the object are introduced as additional information to help detect small objects. Specifically, the FFC in the proposed spectral network extracts global features through its unique Fourier Unit: the spatial feature is first transformed into the frequency domain by the Fast Fourier transform (FFT), then a convolution block is performed to extract the frequency features, obviously this frequency-aware convolution block has a global receptive field in the spatial domain covering the whole image, and finally the features are recovered to the spatial domain using the inverse FFT. To demonstrate the effectiveness of our approach, we conduct experiments on the large remote sensing dataset DIOR, which shows that our approach has excellent performance compared to other detectors. It achieves an average accuracy (mAP) of 73.5% without any tricks.