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

Name: A Parameter-efficient Convolutional Approach for Camouflaged Weed Detection in Multispectral Aerial Imagery
Uploaded: 2026-10-08T22:37:18.351521+00:00
Duration: 407 s
Description: FCBNet is a model for weed segmentation that uses a frozen ConvNeXt backbone and Feature Correction Blocks (FCBs) to achieve high accuracy while drastically reducing computational needs. It outperforms existing models on RGB and multispectral aerial imagery, requiring minimal training time and reducing trainable parameters by over 90%, making it highly efficient for remote sensing tasks.

FCBNet is a model for weed segmentation that uses a frozen ConvNeXt backbone and Feature Correction Blocks (FCBs) to achieve high accuracy while drastically reducing computational needs. It outperforms existing models on RGB and multispectral aerial imagery, requiring minimal training time and reducing trainable parameters by over 90%, making it highly efficient for remote sensing tasks.

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