Abstract
Although existing remote sensing image object detection methods have made significant evolution in deep learning, they did not fully consider the problem of features loss caused by the correspondingly different importance of different channels of feature maps in the convolution pooling. Therefore, a one-stage deep channels attention network for remote sensing images object detection was proposed. First, through a multi-scale feature representation of the Single Shot MultiBox Detector (SSD) Network, the model can combine semantic information with detailed features to better integrate feature layers with different resolutions. Second, for each additional feature extraction layer, the squeeze and excitation (SE) module is introduced, which adaptively re-calibrates the interdependencies between deep channels, then they achieve the response of channel properties in order to learn more efficient feature information. According to experimental results on the RSOD dataset and NWPU VHR-10 dataset, the models proposed in this paper all realize advanced results and achieve state-of-the-art technical performance.
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Acknowledgements
This work is partially supported by the Project of Guangxi Science and Technology (GuiKeAD20159041), the Research Fund of Guangxi Key Lab of Multi-source Information Mining & Security (No.20-A-01–01, No.20-A-01–02, MIMS21-M-01, MIMS20-M-01, MIMS20-04) and the Innovation Project of Guangxi Graduate Education (YCSW2022124); the Guangxi “Bagui” Teams for Innovation and Research, China, the Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing.
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Tang, J., Zhang, W., Zhang, G., Liang, R., Lu, G. (2023). One-Stage Deep Channels Attention Network for Remote Sensing Images Object Detection. In: Li, B., Yue, L., Tao, C., Han, X., Calvanese, D., Amagasa, T. (eds) Web and Big Data. APWeb-WAIM 2022. Lecture Notes in Computer Science, vol 13422. Springer, Cham. https://doi.org/10.1007/978-3-031-25198-6_36
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