Remote Sensing Target Detector with Multi Scale Attention Mechanism

Most of the existing rotation detection models focus on solving problems such as feature misalignment and boundary discontinuity, but ignore the use of contextual information in remote sensing images. However, context information plays a vital role in the accurate detection of small instance targets. Especially when the receptive field of the model is limited, it is very easy to cause deviations in the detection results. Therefore, we propose a Remote Sensing Target Detector with Multi-scale Attention Mechanism Network(MAMNet) that gradually enhances the features in the region of interest by utilizing the spatial, local, and global information of the image input features, thereby supplementing the attention mechanism at multiple scales, better identifying targets, and improving the detection performance of the model. Our model was experimented on DOTAv1.0, DOTAv1.5, HRSC2016 and has achieved the state of the art on DOTAv1.0.