利用多视角图像提升植物缺素识别的跨域适应能力
MV-Match: Multi-View Matching for Domain-Adaptive Identification of Plant Nutrient Deficiencies
- 通过源域和目标域的多视角图像进行无监督域适应
- 在两个数据集上均达到当前最优性能
- 适合农业智能诊断与田间快速检测场景
早期、非侵入性且现场检测营养缺乏对防止因缺素导致的作物重大损失至关重要。虽然标注数据获取成本高昂，但从多个视角采集作物图像却十分简便。尽管这对实际应用具有重要意义，但在源域和目标域均有多个视角的情况下进行无监督域适应的研究仍属空白。本文提出一种利用源域和目标域多视角图像的无监督域适应方法。我们在两个营养缺乏数据集上评估了该方法，结果表明其在两个数据集上的表现均优于其他无监督域适应方法。相关数据集与源代码已公开于 https://github.com/jh-yi/MV-Match。
原文摘要 · Abstract (English)
An early, non-invasive, and on-site detection of nutrient deficiencies is critical to enable timely actions to prevent major losses of crops caused by lack of nutrients. While acquiring labeled data is very expensive, collecting images from multiple views of a crop is straightforward. Despite its relevance for practical applications, unsupervised domain adaptation where multiple views are available for the labeled source domain as well as the unlabeled target domain is an unexplored research area. In this work, we thus propose an approach that leverages multiple camera views in the source and target domain for unsupervised domain adaptation. We evaluate the proposed approach on two nutrient deficiency datasets. The proposed method achieves state-of-the-art results on both datasets compared to other unsupervised domain adaptation methods. The dataset and source code are available at https://github.com/jh-yi/MV-Match.
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