Papers › DecoupleNet: A Lightweight Backbone Network With Efficient Feature Decoupling for...
DecoupleNet: A Lightweight Backbone Network With Efficient Feature Decoupling for Remote Sensing Visual Tasks
Wei Lu, Si-Bao Chen, Qing-Ling Shu, Jin Tang, and Bin Luo
In the realm of computer vision (CV), balancing speed and accuracy remains a significant challenge. Recent efforts have focused on developing lightweight networks that optimize computational efficiency and feature extraction. However, in remote sensing (RS) imagery, where small and multiscale object detection is critical, these networks often fall short in performance. To address these challenges, DecoupleNet is proposed, an innovative lightweight backbone network specifically designed for RS visual tasks in resource-constrained environments. DecoupleNet incorporates two key modules: the feature integration downsampling (FID) module and the multibranch feature decoupling (MBFD) module. The FID module preserves small object features during downsampling, while the MBFD module enhances small and multiscale object feature representation through a novel decoupling approach. Comprehensive evaluations on three RS visual tasks demonstrate DecoupleNet’s superior balance of accuracy and computational efficiency compared to existing lightweight networks. On the NWPU-RESISC45 classification dataset, DecoupleNet achieves a top-1 accuracy of 95.30%, surpassing FasterNet by 2%, with fewer parameters and lower computational overhead. In object detection tasks using the DOTA 1.0 test set, DecoupleNet records an accuracy of 78.04%, outperforming ARC-R50 by 0.69%. For semantic segmentation on the LoveDA test set, DecoupleNet achieves 53.1% accuracy, surpassing UnetFormer by 0.70%. These findings open new avenues for advancing RS image analysis on resource-constrained devices, addressing a pivotal gap in the field. The code and pretrained models are publicly available at https://github.com/lwCVer/DecoupleNet.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | RESISC45 | DecoupleNet D2 | Top 1 Accuracy | 95.87 | #3 of 20 | Archive leaderboard | report |
| Object Detection In Aerial Images | DIOR-R | DecoupleNet D2 | mAP | 67.08 | #9 of 9 | Archive leaderboard | report |
| Object Detection In Aerial Images | DOTA | DecoupleNet D2 | mAP | 78.04% | #29 of 58 | Archive leaderboard | report |
| Semantic Segmentation | LoveDA | DecoupleNet D2 | Category mIoU | 53.1 | #13 of 19 | Archive leaderboard | report |
| Semantic Segmentation | UAVid | DecoupleNet D2 | Mean IoU | 65.8 | #9 of 10 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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