SWNet：一种用于伪装杂草检测的跨光谱网络
SWNet: A Cross-Spectral Network for Camouflaged Weed Detection
AI总结：
SWNet通过融合可见光和近红外信息，利用光谱差异提升复杂农田中伪装杂草的检测精度，实验表明其优于十种现有方法。
摘要：
本文提出了SWNet，一种双模端到端跨光谱网络，专门用于复杂农业环境中伪装杂草的检测。植物伪装，其特征是同色混合，其中入侵物种模仿主要作物的表型特征，对传统计算机视觉系统构成重大挑战。为克服这些限制，SWNet利用Pyramid Vision Transformer v2骨干网络捕捉长距离依赖关系，并采用双模门控融合模块动态整合可见光和近红外信息。通过利用近红外光谱中捕获的叶绿素反射差异，所提出的架构有效区分在可见范围内无法区分的目标。此外，采用边缘感知细化模块以产生更清晰的目标边界并减少结构模糊。在Weeds-Banana数据集上的实验结果表明，SWNet优于十种最先进的方法。研究证明，跨光谱数据与边界引导细化的整合对于复杂作物冠层中的高分割精度至关重要。代码可在GitHub上获得：https://cod-espol.github.io/SWNet/
英文摘要：
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/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
