A Parameter-efficient Convolutional Approach for Camouflaged Weed Detection in Multispectral Aerial Imagery
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Abstract (arXiv)
We introduce FCBNet, an efficient model designed for camouflaged weed detection. The architecture is based on a fully frozen ConvNeXt backbone, the proposed Feature Correction Block (FCB), which leverages efficient convolutions for feature refinement, and a lightweight decoder. FCBNet is evaluated on the WeedBananaCOD and WeedMap datasets under both RGB and multispectral modalities, showing that FCBNet outperforms models such as U-Net, DeepLabV3+, SK-U-Net, SegFormer, and WeedSense in terms of mIoU, exceeding 85%, while also achieving superior computational efficiency, requiring only 0.06 to 0.2 hours for training. Furthermore, the frozen backbone strategy reduces the number of trainable parameters by more than 90%, significantly lowering memory requirements. Code available at: https://github.com/Leo-Thomas/fcbnet
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 15 of its 47 references that are in the map. First posted on arXiv 28 Feb 2026.
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15 of 47 references.
References · 15
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3 more
- 2022Segmenting across places: The need for fair transfer learning with satellite imagerycited by 6
- 2025WeedSense: Multi-Task Learning for Weed Segmentation, Height Estimation, and Growth Stage Classificationcited by 4
- 2026Multi-encoder ConvNeXt Network with Smooth Attentional Feature Fusion for Multispectral Semantic Segmentationcited by 3
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Citing papers per year: 2026: 1.
1 paper.