发表机构
National Institute of Technology Delhi; Indian Institute of Technology Bombay（德里国家技术学院; 孟买印度理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对农业图像语义分割实际部署问题，提出扩散引导混合分割框架，通过多种实验评估，结果显示最佳模型标注效率高，适应后模型可转移，表明合理配对主干和细化器并结合扰动感知再训练可提升分割效果和鲁棒性。
AI 中文摘要
农业图像中的语义分割通常在域内协议下进行评估，但实际部署需要对外观扰动、有限标注和跨域转移具有鲁棒性。本文提出了一种扩散引导的混合分割框架，其中U-Net、DeepLabV3+和SegFormer主干生成粗掩码，再由去噪扩散概率模型（DDPM）、潜在扩散或语义引导扩散进行细化。通过对PlantSegV3进行3x3架构筛选研究等一系列实验评估该框架。结果表明，最佳混合模型在标注大幅减少时仍保持稳定，具有很强的标注效率，且适应后的模型能有效转移到外部数据集，证明扩散细化和边界感知优化提供了可转移的结构先验。
英文摘要
Semantic segmentation in agricultural imagery is often evaluated under in-domain protocols, yet practical deployment requires robustness to appearance perturbations, limited annotations, and cross domain shift. This paper presents a diffusion-guided hybrid segmentation framework in which U-Net, DeepLabV3+, and SegFormer backbones generate coarse masks that are refined by Denoising Diffusion Probabilistic Models (DDPM), latent diffusion, or semantic-guided diffusion. The framework is evaluated through a 3x3 architectural screening study on PlantSegV3, followed by boundary-constrained optimization, perturbation-guided retraining, low-data evaluation, constrained hyperparameter screening, and controlled cross-domain adaptation. On PlantSegV3, the best selected hybrid model achieves 71.83% refined mean Intersection-over-Union (mIoU) and 26.10% refined Boundary-F1, and the selected models remain stable under substantially reduced supervision, demonstrating strong annotation efficiency. Perturbation analysis identifies grayscale conversion, fog, coarse dropout, and shadow as the most disruptive appearance shifts, and the resulting augmentation policy substantially improves robustness during retraining. The adapted models further show effective transfer to external agricultural datasets under limited target supervision, indicating that diffusion refinement and boundary-aware optimization provide transferable structural priors. Overall, the results show that carefully matched backbone-refiner pairings, combined with perturbation-aware retraining, can improve structural delineation and robustness under realistic resource and distribution constraints.
