PDF(Pubmed)
Abstract：
Shadow, a natural phenomenon resulting from the absence of light, plays a pivotal role in agriculture, particularly in processes such as photosynthesis in plants. Despite the availability of generic shadow datasets, many suffer from annotation errors and lack detailed representations of agricultural shadows with possible human activity inside, excluding those derived from satellite or drone views. In this paper, we present an evaluation of a synthetically generated top-down shadow segmentation dataset characterized by photorealistic rendering and accurate shadow masks. We aim to determine its efficacy compared to real-world datasets and assess how factors such as annotation quality and image domain influence neural network model training. To establish a baseline, we trained numerous baseline architectures and subsequently explored transfer learning using various freely available shadow datasets. We further evaluated the out-of-domain performance compared to the training set of other shadow datasets. Our findings suggest that AgroSegNet demonstrates competitive performance and is effective for transfer learning, particularly in domains similar to agriculture.
摘要：
影子,由于没有光导致的自然现象，在农业中起着举足轻重的作用，特别是在植物光合作用等过程中。尽管有通用影子数据集，许多人遭受注释错误，并且缺乏内部可能存在人类活动的农业阴影的详细表示，不包括来自卫星或无人机视图的那些。在本文中,我们提供了一个综合生成的自上而下的阴影分割数据集的评估，其特征是逼真的渲染和精确的阴影掩模。我们的目标是确定其与现实世界数据集相比的功效，并评估注释质量和图像域等因素如何影响神经网络模型训练。要建立基线，我们训练了许多基线架构，随后使用各种免费的影子数据集探索了迁移学习。与其他阴影数据集的训练集相比，我们进一步评估了域外性能。我们的研究结果表明，AgroSegNet表现出竞争力，对迁移学习是有效的，特别是在类似于农业的领域。
