Division of Electronics and Information Engineering, Jeonbuk National University, Jeonju-si, 54896, Republic of Korea.
Core Research Institute of Intelligent Robots, Jeonbuk National University, Jeonju-si, 54896, Republic of Korea.
Sci Rep. 2022 May 23;12(1):8672. doi: 10.1038/s41598-022-12532-7.
Fine segmentation labelling tasks are time consuming and typically require a great deal of manual labor. This paper presents a novel method for efficiently creating pixel-level fine segmentation labelling that significantly reduces the amount of necessary human labor. The proposed method utilizes easily produced multiple and complementary coarse labels to build a complete fine label via supervised learning. The primary label among the coarse labels is the manual label, which is produced with simple contours or bounding boxes that roughly encompass an object. All others coarse labels are complementary and are generated automatically using existing algorithms. Fine labels can be rapidly created during the supervised learning of such coarse labels. In the experimental study, the proposed technique achieved a fine label IOU (intersection of union) of 92% in segmenting our newly constructed bean field dataset. The proposed method also achieved 95% and 92% mean IOU when tested on publicly available agricultural CVPPP and CWFID datasets, respectively. Our proposed method of segmentation also achieved a mean IOU of 81% when it was tested on our newly constructed paprika disease dataset, which includes multiple categories.
精细分割标注任务耗时且通常需要大量人工。本文提出了一种高效创建像素级精细分割标注的新方法，可显著减少所需的人工劳动量。所提出的方法利用易于生成的多个互补粗标签，通过监督学习构建完整的精细标签。粗标签中的主要标签是手动标签，它使用简单的轮廓或大致包含对象的边界框生成。所有其他粗标签都是补充标签，使用现有算法自动生成。在监督学习这些粗标签的过程中，可以快速创建精细标签。在实验研究中，所提出的技术在分割我们新构建的豆田数据集时实现了 92%的精细标签 IOU（交并比）。当在公开可用的农业 CVPPP 和 CWFID 数据集上进行测试时，该方法分别实现了 95%和 92%的平均 IOU。当在我们新构建的包括多个类别的辣椒病害数据集上进行测试时，我们提出的分割方法也实现了 81%的平均 IOU。
