论文标题
多种果花种类的全景分割的自学学习
Self-supervised Learning for Panoptic Segmentation of Multiple Fruit Flower Species
论文作者
论文摘要
使用手动生成标签训练的卷积神经网络通常用于语义或实例分割。在精确的农业中，自动化花检测方法使用监督模型和后处理技术，这些技术可能不会始终如一地表现为花朵的出现，并且数据采集条件各不相同。我们提出了一种自我监督的学习策略，以使用自动生成的伪标签来增强分割模型对不同花种类的敏感性。我们采用数据增强和完善方法来提高模型预测的准确性。然后将增强的语义预测转换为全磁伪标记，以迭代训练多任务模型。可以通过现有的后处理方法来完善自我监督的模型预测，以进一步提高其准确性。对多物种果树花数据集的评估表明，我们的方法的表现优于最先进的模型，而无需计算昂贵的后处理步骤，为花朵检测应用提供了新的基线。
Convolutional neural networks trained using manually generated labels are commonly used for semantic or instance segmentation. In precision agriculture, automated flower detection methods use supervised models and post-processing techniques that may not perform consistently as the appearance of the flowers and the data acquisition conditions vary. We propose a self-supervised learning strategy to enhance the sensitivity of segmentation models to different flower species using automatically generated pseudo-labels. We employ a data augmentation and refinement approach to improve the accuracy of the model predictions. The augmented semantic predictions are then converted to panoptic pseudo-labels to iteratively train a multi-task model. The self-supervised model predictions can be refined with existing post-processing approaches to further improve their accuracy. An evaluation on a multi-species fruit tree flower dataset demonstrates that our method outperforms state-of-the-art models without computationally expensive post-processing steps, providing a new baseline for flower detection applications.
