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Abstract
Object detection for remote sensing images remains a challenging problem. In this paper, we proposed an effective remote sensing detection network (RS-Net) based on YOLOv4, which greatly solves the difficulties of remote sensing object detection. Center to our RS-Net is coarse-grained highlighting (CGH), fine-grained mining (FGM) and scale distillation (SD) modules. Through a fusion of original and complementary feature, CGH alleviates the dilemma of object semantic extraction caused by the variability of background information. Besides, FGM is designed to further enhance the feature extraction ability, which is implemented by prominent branch and ensemble branch in parallel. The former highlights the object semantics of being suppressed due to its high similarity with background. The latter mines object information as a whole to reduce the interference from noise. Finally, SD significantly improves the richness of small object information in shallow layer. Experiments conducted over DOTA and NWPU-VHR datasets confirm that the proposed RS-Net achieves competitive detection performance compared with state-of-the-art detectors.
Highlights
With the development of remote sensing technology, increasing number of remote sensing images are produced every day
Experimental Results experiments we have conducted to evaluate the effectiveness of the proposed remote sensing detection network (RS-Net) will be introduced in details, including the description of DOTA dataset, the implementation details, the experimental result on DOTA and the ablation studies conducted with the proposed method
A Dataset Setting We evaluate our method on two popular datasets i.e., DOTA and NWPU-VHR
Summary
With the development of remote sensing technology, increasing number of remote sensing images are produced every day. Even little noise may destroy the original weak feature representation, which are effectively alleviated by the proposed fine-grained mining module
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