用扩散模型自动生成农业图像与分割掩码，解决数据不足问题
SynthSet: Generative Diffusion Model for Semantic Segmentation in Precision Agriculture
- 双扩散架构结合DDPM与GAN，自动合成逼真带标注的农田图像
- 合成数据训练的模型在真实小麦田数据集上表现良好
- 适用于多种农业分割任务，无需人工标注
本文提出一种生成合成标注数据的方法，以应对精准农业中语义分割任务的数据稀缺问题。采用去噪扩散概率模型（DDPM）和生成对抗网络（GAN），构建双扩散模型架构，实现无需人工干预的逼真农业图像-掩码对合成。通过超分辨率技术增强合成图像的表型特征及其与掩码的一致性。以小麦穗分割为例，验证了方法的有效性。合成数据质量高，表明该方法能有效生成高质量图像-掩码对。在外部多样化的实拍小麦田数据集上测试，基于合成数据训练的模型表现出良好性能。结果证明该方法对缓解语义分割任务中的数据短缺具有显著效果，且可轻松拓展至其他精准农业分割任务。
原文摘要 · Abstract (English)
This paper introduces a methodology for generating synthetic annotated data to address data scarcity in semantic segmentation tasks within the precision agriculture domain. Utilizing Denoising Diffusion Probabilistic Models (DDPMs) and Generative Adversarial Networks (GANs), we propose a dual diffusion model architecture for synthesizing realistic annotated agricultural data, without any human intervention. We employ super-resolution to enhance the phenotypic characteristics of the synthesized images and their coherence with the corresponding generated masks. We showcase the utility of the proposed method for wheat head segmentation. The high quality of synthesized data underscores the effectiveness of the proposed methodology in generating image-mask pairs. Furthermore, models trained on our generated data exhibit promising performance when tested on an external, diverse dataset of real wheat fields. The results show the efficacy of the proposed methodology for addressing data scarcity for semantic segmentation tasks. Moreover, the proposed approach can be readily adapted for various segmentation tasks in precision agriculture and beyond.
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