论文标题
叶片细分并深入学习：在模型确定性，测试时间增加，权衡取舍
Leaf Segmentation and Counting with Deep Learning: on Model Certainty, Test-Time Augmentation, Trade-Offs
论文作者
论文摘要
植物表型任务（例如叶片分割和计数）是对表型特征的研究。由于它非常适合这些任务，因此在最近的著作中，深入监督的学习一直很普遍，提议在细分和计数叶子上表现更好的模型。尽管研究小组做出了良好的努力，但提出更好方法的主要挑战之一仍然是标记数据可用性的限制。该领域的主要努力似乎是在增加现有的有限数据集，并且模型过程的某些方面被宣布不足。本文探讨了这些主题和当前的实验，这些主题导致了叶片分割挑战中表现最佳的方法的发展，以及在Komatsuna植物的另一个外部数据集中。该模型具有竞争性能，而可以说比其他最近提出的竞争性能要简单。这些实验还带来了洞察力，例如，模型的基数和测试时间扩大可能在单个类和高闭塞的对象分割以及最近提出的用于基准测试的数据集的数据分布中具有强大的应用。
Plant phenotyping tasks such as leaf segmentation and counting are fundamental to the study of phenotypic traits. Since it is well-suited for these tasks, deep supervised learning has been prevalent in recent works proposing better performing models at segmenting and counting leaves. Despite good efforts from research groups, one of the main challenges for proposing better methods is still the limitation of labelled data availability. The main efforts of the field seem to be augmenting existing limited data sets, and some aspects of the modelling process have been under-discussed. This paper explores such topics and present experiments that led to the development of the best-performing method in the Leaf Segmentation Challenge and in another external data set of Komatsuna plants. The model has competitive performance while been arguably simpler than other recently proposed ones. The experiments also brought insights such as the fact that model cardinality and test-time augmentation may have strong applications in object segmentation of single class and high occlusion, and regarding the data distribution of recently proposed data sets for benchmarking.
