Multi-Scale Structure-Conditioned Feature Transform Network for Object Detection in Remote Sensing Imagery

2021 
In recent years, object detection in remote sensing imagery has attracted more and more attention. Accurate object detection in remote sensing imagery, especially for small objects, is still challenging. Most existing methods utilize the global information in the deep fully convolution layer and neglect the local information in the input image. However, the local information contains sufficient spatial information, which is beneficial for precise localization. Additionally, there still exists variable factors, such as the arbitrary aspect ratio and rotation, which interfere the object detection performance. To solve these problems mentioned above, we propose a novel multi-scale structure-conditioned feature transform network, which adopts FCOS as the baseline and ATSS as the method for training sample selection. On one hand, structural information is extracted to represent the spatial semantic information. On the other hand, multi-scale information is enhanced through a novel hierarchical residual-in-residual module. Experiments on the HRRSD data set have demonstrated the superiority of our method.
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