MERGENET: FEATURE-MERGED NETWORK FOR MULTI-SCALE OBJECT DETECTION IN REMOTE SENSING IMAGES

Object detection has been playing a significant role in the field of remote sensing for a long period while it is still full of challenges. The biggest one is how to detect multi-scale objects with high accuracy and fast speed in remote sensing images. One-stage object detectors have been achieving relatively high accuracy and efficiency with small memory footprint. However, they have a not very well performance on small objects. In this paper, we discuss the importance of the context information between feature maps in different scales which is helpful for detecting small objects. Especially, we propose a Feature-merged detection networks (MergeNet), which can be inserted into the one-stage detectors easily, to unify the multi-scale feature and context information effectively. Experiments on DOTA dataset demonstrate that our model can significantly improve the performance of the one-stage method.