Frequency Spectrum Features Modeling for Real-Time Tiny Object Detection in Remote Sensing Image

Recently, object detection in remote sensing images has achieved rapid advancement. However, due to critical issues, such as low spatial resolution and complex background noises, it is still difficult to achieve satisfactory object detection performance for remote sensing images. For current widely used object detection methods, the feature resolution of the backbone network is decreased gradually with successive pooling operations. In this way, object spatial details are largely lost for the deeper feature layers, resulting in the difficulty of accurate object detection, especially for tiny objects. However, current methods fail to eliminate the adverse effects due to the loss of object details. To this end, considering that high-frequency information is more likely to be overlooked and high-frequency object details may be beneficial for detecting tiny objects, we propose to improve the previous spatial feature modeling pipeline with the learned features in the frequency domain. Specifically, discrete cosine transform (DCT) is first used to transform the original image into the frequency domain, obtaining the corresponding frequency spectrum features. We then utilize a dual-domain feature extraction (DFE) network based on a lightweight attention mechanism to align the features in two different domains. Finally, a domain synergy fusion (DSF) module is further employed to match and fuse the features in the spatial domain and the obtained features in the frequency domain. Extensive experimental results are obtained on the challenging remote sensing datasets, DIOR and DOTA. Experimental results show that our method can increase at least 2.9% ${\mathbf {AP}}_{\mathbf {s}}^{\mathbf {50}}$ in DIOR and 3.5% ${\mathbf {AP}}_{\mathbf {s}}^{\mathbf {50}}$ in DOTA compared to the new state-of-the-art methods, which effectively demonstrates the superiority of our proposed method.