论文标题
查找浆果：使用点监督和塑造先验的蔓越莓进行分割和计数
Finding Berries: Segmentation and Counting of Cranberries using Point Supervision and Shape Priors
论文作者
论文摘要
精确农业已通过向决策者提供基本信息来增加农作物产量的关键因素。在这项工作中，我们提出了一种深度学习方法，用于同时分割和对蔓越莓进行计数，以帮助产量估计和太阳暴露预测。值得注意的是，使用低成本中心点注释进行监督。该方法称为Triple-S网络，与形状先验的三部分损失结合在一起，以更好地适合农业场景中典型的已知形状对象。与最先进的情况相比，我们的结果将整体细分性能提高了6.74％以上，并将结果计数结果提高了22.91％。为了训练和评估网络，我们收集了蔓越莓空中图像数据集（CRAID），这是蔓越莓田中最大的空中无人机图像数据集。该数据集将公开可用。
Precision agriculture has become a key factor for increasing crop yields by providing essential information to decision makers. In this work, we present a deep learning method for simultaneous segmentation and counting of cranberries to aid in yield estimation and sun exposure predictions. Notably, supervision is done using low cost center point annotations. The approach, named Triple-S Network, incorporates a three-part loss with shape priors to promote better fitting to objects of known shape typical in agricultural scenes. Our results improve overall segmentation performance by more than 6.74% and counting results by 22.91% when compared to state-of-the-art. To train and evaluate the network, we have collected the CRanberry Aerial Imagery Dataset (CRAID), the largest dataset of aerial drone imagery from cranberry fields. This dataset will be made publicly available.
