Spatial-Transformer and Cross-Scale Fusion Network (STCS-Net) for Small Object Detection in Remote Sensing Images
Author:
Publisher
Springer Science and Business Media LLC
Subject
Earth and Planetary Sciences (miscellaneous),Geography, Planning and Development
Link
https://link.springer.com/content/pdf/10.1007/s12524-023-01709-w.pdf
Reference33 articles.
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2. Bodla, N., Singh, B., Chellappa, R., & Davis, L. S. (2017). Soft-NMS–improving object detection with one line of code. In Proceedings of the IEEE international conference on computer vision (pp. 5561–5569). https://doi.org/10.1109/ICCV.2017.593
3. Cai, Z., & Vasconcelos, N. (2018). Cascade r-cnn: Delving into high quality object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 6154–6162). https://doi.org/10.1109/CVPR.2018.00644
4. Chen, Z., Yang, C., Li, Q., Zhao, F., Zha, Z. J., & Wu, F. (2021). Disentangle your dense object detector. In Proceedings of the 29th ACM international conference on multimedia (pp. 4939–4948). https://doi.org/10.1145/3474085.3475351
5. Devi, N. B., Kavida, A. C., & Murugan, R. (2022). Feature extraction and object detection using fast-convolutional neural network for remote sensing satellite image. Journal of the Indian Society of Remote Sensing, 50, 961–973. https://doi.org/10.1007/s12524-022-01506-x
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