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- W4301184452 abstract "Deep learning-based object detection in remote sensing images is an important yet challenging task because of the complex background and large variations in size of the targets. Currently, many detectors have made significant progress in improving detection accuracy, but they have not shown good performance in terms of detection speed and model size. To address these issues, a lightweight and efficient object detector in remote sensing images is proposed. Specifically, we utilize MobileNetv3 with Shuffle Attention as the feature extraction backbone network to reduce the parameter of the model. Meanwhile, deformable convolution is introduced to adapt to the deformation of the target and obtain stronger geometric feature expression ability. Extensive experiments on two remote sensing public datasets (RSOD and DIOR) show good performance for the efficiency of our detector. In particular, our strategy achieves 71.3 mAP on the DIOR dataset with 3.05 parameters and a test speed of 12.1 ms, and it also has good performance on the RSOD dataset." @default.
- W4301184452 created "2022-10-04" @default.
- W4301184452 creator A5029278837 @default.
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- W4301184452 date "2022-08-19" @default.
- W4301184452 modified "2023-09-27" @default.
- W4301184452 title "Efficient Object Detection with Deformable Convolution for Optical Remote Sensing Imagery" @default.
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- W4301184452 doi "https://doi.org/10.1109/prai55851.2022.9904075" @default.
- W4301184452 hasPublicationYear "2022" @default.
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