A Parameter-efficient Convolutional Approach for Camouflaged Weed Detection in Multispectral Aerial Imagery
Leo Thomas RamosAngel D. Sappa
Oct 2026
Artificial IntelligenceComputer Vision
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: https://github.com/Leo-Thomas/fcbnet
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