End-to-end Remote Sensing Image Target Detection Based on Rotate Denoise Transformer
An end-to-end remote sensing image rotation target detection method based on dense grid argumentation and rotate denoise Transformer is proposed.Firstly,in response to the problem of object cutoff during sliding window slic-ing of remote sensing images,which affects network learning,a remote sensing image data argumentation method based on dense grids is proposed to improve the network's prediction ability for incomplete objects.Then,an end-to-end detec-tion network based on the rotate denoise Transformer architecture is proposed,using PVTv2 to extract multi-scale features,and using the Transformer encoder to aggregate and enhance the features.This paper decodes the target by a Trans-former decoder,and directly achieve rotate object prediction through classification and regression heads,which is called end-to-end detection.Lastly,this paper proposes a rotate contrast denoise learning method,which is used for rotate ob-ject detection and accelerates network convergence.The experiment results on the data of the National Big data and Com-putational Intelligence Challenge 2023 shows that the mAP of the proposed method in this paper reached 98.38%.
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