D4：用于葡萄园嫩梢检测的基于文本引导扩散模型的领域自适应数据增强
D4: Text-guided diffusion model-based domain adaptive data augmentation for vineyard shoot detection
AI总结：
本文提出D4生成式数据增强方法，利用文本引导扩散模型生成适应目标领域的带标注葡萄园嫩梢图像，显著提升BBox与关键点检测性能。
摘要：
在农业领域，利用目标检测模型进行植物表型分析正受到关注。然而，由于标注困难和领域多样性，收集创建通用且高精度模型所需的训练数据极具挑战性。此外，训练数据难以在不同作物之间迁移；尽管已经开发出对特定环境、条件或作物有效的机器学习模型，但它们无法在实际田间得到广泛应用。在本研究中，我们提出了一种用于葡萄园嫩梢检测的生成式数据增强方法（D4）。D4使用预训练的文本引导扩散模型，该模型基于从无人地面车辆或其他方式采集的视频数据中筛选出的大量原始图像，以及少量带标注数据集。所提出的方法在保留目标检测所需标注信息的同时，生成具有适应目标领域背景信息的新带标注图像。此外，D4克服了农业中训练数据不足的问题，包括标注困难和领域多样性。我们证实，对于葡萄园嫩梢检测，这种生成式数据增强方法在BBox检测任务上将平均精度均值最高提升了28.65%，在关键点检测任务上将平均精度最高提升了13.73%。我们的生成式数据增强方法D4有望同时解决农业训练数据生成的成本和领域多样性问题，并提升检测模型的泛化性能。
英文摘要：
In an agricultural field, plant phenotyping using object detection models is gaining attention. However, collecting the training data necessary to create generic and high-precision models is extremely challenging due to the difficulty of annotation and the diversity of domains. Furthermore, it is difficult to transfer training data across different crops, and although machine learning models effective for specific environments, conditions, or crops have been developed, they cannot be widely applied in actual fields. In this study, we propose a generative data augmentation method (D4) for vineyard shoot detection. D4 uses a pre-trained text-guided diffusion model based on a large number of original images culled from video data collected by unmanned ground vehicles or other means, and a small number of annotated datasets. The proposed method generates new annotated images with background information adapted to the target domain while retaining annotation information necessary for object detection. In addition, D4 overcomes the lack of training data in agriculture, including the difficulty of annotation and diversity of domains. We confirmed that this generative data augmentation method improved the mean average precision by up to 28.65% for the BBox detection task and the average precision by up to 13.73% for the keypoint detection task for vineyard shoot detection. Our generative data augmentation method D4 is expected to simultaneously solve the cost and domain diversity issues of training data generation in agriculture and improve the generalization performance of detection models.
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。
