The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.
ABNet: Adaptive Balanced Network for Multiscale Object Detection in Remote Sensing Imagery
Abstract
Benefiting from the development of convolutional neural networks (CNNs), many excellent algorithms for object detection have been presented. Remote sensing object detection (RSOD) is a challenging task mainly due to: 1) complicated background of remote sensing images (RSIs) and 2) extremely imbalanced scale and sparsity distribution of remote sensing objects. Existing methods cannot effectively solve these problems with excellent detection accuracy and rapid speed. To address these issues, we propose an adaptive balanced network (ABNet) in this article. First, we design an enhanced effective channel attention (EECA) mechanism to improve the feature representation ability of the backbone, which can alleviate the obstacles of complex background on foreground objects. Then, to combine multiscale features adaptively in different channels and spatial positions, an adaptive feature pyramid network (AFPN) is designed to capture more discriminative features. Furthermore, considering that the original FPN ignores rich deep-level features, a context enhancement module (CEM) is proposed to exploit abundant semantic information for multiscale object detection. Experimental results on three public datasets demonstrate that our approach exhibits superior performance over baseline by only introducing less than 1.5M extra parameters.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
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
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581
CBAM: Convolutional Block Attention Module
Woo, S., Park, J., Lee, J.-Y., & Kweon, I. S. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_1 · Cited by 25,853
YOLO9000: Better, Faster, Stronger
Redmon, J., & Farhadi, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.690 · Cited by 19,892
A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper)
Khanam, Z., Achari, V. P. S., Boukhennoufa, I., Jindal, A., & Singh, A. K. (2024) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/oasics.ng-res.2024.2 · Cited by 14,040
Wang, X., Girshick, R., Gupta, A., & He, K. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00813 · Cited by 11,596
Metadata
- Authors
- Liu, Y., Li, Q., Yuan, Y., Du, Q., & Wang, Q.
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2022
- Volume
- 60
- Pages
- 1-14
- ISSN
- 0196-2892 (ISSN-L)
- 1558-0644
- OpenAlex
- W4205932092
- Cited by
- 258
- References
- 56
- FWCI
- 14.73
- Type
- journal article
- Language
- en
- Open access
- closed
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- Liu, Y., Li, Q., Yuan, Y., Du, Q., & Wang, Q. (2022). ABNet: Adaptive Balanced Network for Multiscale Object Detection in Remote Sensing Imagery. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/tgrs.2021.3133956
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