This paper presents SWNet, a bimodal end-to-end cross-spectral network specifically engineered for the detection of camouflaged weeds in dense agricultural environments. Plant camouflage, characterized by homochromatic blending where invasive species mimic the phenotypic traits of primary crops, poses a significant challenge for traditional computer vision systems. To overcome these limitations, SWNet utilizes a Pyramid Vision Transformer v2 backbone to capture long-range dependencies and a Bimodal Gated Fusion Module to dynamically integrate Visible and Near-Infrared information. By leveraging the physiological differences in chlorophyll reflectance captured in the NIR spectrum, the proposed architecture effectively discriminates targets that are otherwise indistinguishable in the visible range. Furthermore, an Edge-Aware Refinement module is employed to produce sharper object boundaries and reduce structural ambiguity. Experimental results on the Weeds-Banana dataset indicate that SWNet outperforms ten state-of-the-art methods. The study demonstrates that the integration of cross-spectral data and boundary-guided refinement is essential for high segmentation accuracy in complex crop canopies. The code is available on GitHub: https://cod-espol.github.io/SWNet/

翻译：本文提出SWNet——一种专为密集农业环境中伪装杂草检测设计的双模态端到端跨光谱网络。植物伪装现象，即入侵物种通过模拟主栽作物的表型特征进行同色融合，对传统计算机视觉系统构成重大挑战。为克服上述限制，SWNet采用金字塔视觉变换器v2骨干网络捕获长距离依赖关系，并利用双模态门控融合模块动态整合可见光与近红外信息。通过利用近红外光谱中捕获的叶绿素反射生理差异，该架构有效区分了在可见光范围内难以辨别的目标。此外，边缘感知细化模块被用于生成更清晰的目标边界并减少结构模糊性。在Weeds-Banana数据集上的实验结果表明，SWNet的性能优于十种现有最优方法。本研究证实，跨光谱数据集成与边界引导细化对于复杂作物冠层中的高精度分割至关重要。代码已在GitHub开源：https://cod-espol.github.io/SWNet/