用可见光与近红外融合检测伪装杂草，提升农田识别精度。
SWNet: A Cross-Spectral Network for Camouflaged Weed Detection
- 双模态融合网络，结合可见光与近红外特征增强分辨力。
- 在香蕉田数据集上超越10种先进方法，分割准确率显著提升。
- 适合农业自动化场景，尤其对隐蔽杂草检测有实际价值。
本文提出SWNet，一种专为密集农田环境中的伪装杂草检测设计的双模态端到端跨光谱网络。植物伪装表现为入侵物种在表型上模仿主作物，导致传统计算机视觉系统难以区分。为克服此挑战，SWNet采用Pyramid Vision Transformer v2骨干网络捕捉长距离依赖，并引入双模态门控融合模块，动态整合可见光与近红外信息。利用近红外波段中叶绿素反射的生理差异，该架构有效区分在可见光下无法辨别的目标。此外，边缘感知精修模块生成更锐利的目标边界，降低结构模糊性。在Weeds-Banana数据集上的实验表明，SWNet优于十种先进方法。研究证实，跨光谱数据融合与边界引导精修对复杂作物冠层中的高精度分割至关重要。代码已开源：https://cod-espol.github.io/SWNet/
原文摘要 · Abstract (English)
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/
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