A Parameter-efficient Convolutional Approach for Camouflaged Weed Detection in Multispectral Aerial Imagery

Abstract: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: this https URL

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)

arXiv:2603.06655 [cs.CV]

(or arXiv:2603.06655v2 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2603.06655 Focus to learn more arXiv-issued DOI via DataCite

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1349-1358, 2026

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