The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Feature-Attentioned Object Detection in Remote Sensing Imagery
Abstract
In this work, we introduce a novel feature-attentioned object detection framework to boost its performance in remote sensing imagery, which can focus on learning these intrinsic representations from different aspects in an end-to-end framework. Firstly, when fusing multi-scale visual features of backbone network, we adopt the channel-wise and pixel-wise attentions to enhance these object-related representations and weaken the background/noise information. Secondly, an adaptive multiple receptive fields attention mechanism is employed to generate horizontal region proposals under the special situation where objects in the remote sensing imagery are always with different aspect ratios. Finally, the proposal-level feature attention is proposed to better consider both multi-layer convolutional and apparent representations so that the region of interest network can better predict the object-wise category and its corresponding location information. Comprehensive evaluations on DOTA and UCAS-AOD datasets well demonstrate the effectiveness of our feature-attentioned network for object detection in remote sensing imagery.
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Cites
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ImageNet classification with deep convolutional neural networks
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803
Squeeze-and-Excitation Networks
Hu, J., Shen, L., & Sun, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00745 · Cited by 35,667
Feature Pyramid Networks for Object Detection
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939
Squeeze-and-Excitation Networks
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Path Aggregation Network for Instance Segmentation
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Cited by
Oriented R-CNN for Object Detection
Xie, X., Cheng, G., Wang, J., Yao, X., & Han, J. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00350 · Cited by 1,351
R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object
Yang, X., Yan, J., Feng, Z., & He, T. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i4.16426 · Cited by 1,238
A review on deep learning in UAV remote sensing
Osco, L. P., Marcato Junior, J., Marques Ramos, A. P., de Castro Jorge, L. A., Fatholahi, S. N., de Andrade Silva, J., Matsubara, E. T., Pistori, H., Gonçalves, W. N., & Li, J. (2021) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2021.102456 · Cited by 458
Yang, X., Yan, J., Liao, W., Yang, X., Tang, J., & He, T. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3166956 · Cited by 365
Shape-Adaptive Selection and Measurement for Oriented Object Detection
Hou, L., Lu, K., Xue, J., & Li, Y. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i1.19975 · Cited by 298
Metadata
- Authors
- Li, C., Xu, C., Cui, Z., Wang, D., Zhang, T., & Yang, J.
- Venue
- 2019 IEEE International Conference on Image Processing (ICIP)
- Publisher
- IEEE
- Published
- 2019-09
- Pages
- 3886-3890
- ISSN
- 1522-4880 (ISSN-L)
- OpenAlex
- W2970370255
- Cited by
- 164
- References
- 10
- FWCI
- 6.66
- Type
- conference paper
- Language
- en
- Open access
- closed
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