Zhang Wenli, Liu Yuxin, Zheng Chao, Cui Guoqiang, Guo Wei
Information Department, Beijing University of Technology, Beijing 100022, China.
Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 188-0002, Japan.
Plant Phenomics. 2023 Jul 27;5:0067. doi: 10.34133/plantphenomics.0067. eCollection 2023.
Although deep learning-based fruit detection techniques are becoming popular, they require a large number of labeled datasets to support model training. Moreover, the manual labeling process is time-consuming and labor-intensive. We previously implemented a generative adversarial network-based method to reduce labeling costs. However, it does not consider fitness among more species. Methods of selecting the most suitable source domain dataset based on the fruit datasets of the target domain remain to be investigated. Moreover, current automatic labeling technology still requires manual labeling of the source domain dataset and cannot completely eliminate manual processes. Therefore, an improved EasyDAM_V3 model was proposed in this study as an automatic labeling method for additional classes of fruit. This study proposes both an optimal source domain establishment method based on a multidimensional spatial feature model to select the most suitable source domain, and a high-volume dataset construction method based on transparent background fruit image translation by constructing a knowledge graph of orchard scene hierarchy component synthesis rules. The EasyDAM_V3 model can automatically obtain fruit label information from the dataset, thereby eliminating manual labeling. To test the proposed method, pear was used as the selected optimal source domain, followed by orange, apple, and tomato as the target domain datasets. The results showed that the average precision of annotation reached 90.94%, 89.78%, and 90.84% for the target datasets, respectively. The EasyDAM_V3 model can obtain the optimal source domain in automatic labeling tasks, thus eliminating the manual labeling process and reducing associated costs and labor.
尽管基于深度学习的水果检测技术越来越流行，但它们需要大量的标注数据集来支持模型训练。此外，人工标注过程既耗时又费力。我们之前实现了一种基于生成对抗网络的方法来降低标注成本。然而，它没有考虑更多物种之间的适应性。基于目标域水果数据集选择最合适源域数据集的方法仍有待研究。此外，当前的自动标注技术仍然需要对源域数据集进行人工标注，无法完全消除人工流程。因此，本研究提出了一种改进的EasyDAM_V3模型作为额外水果类别的自动标注方法。本研究既提出了一种基于多维空间特征模型的最优源域建立方法来选择最合适的源域，又提出了一种基于果园场景层次组件合成规则构建知识图谱进行透明背景水果图像翻译的大容量数据集构建方法。EasyDAM_V3模型可以从数据集中自动获取水果标签信息，从而消除人工标注。为了测试所提出的方法，选择梨作为最优源域，随后将橙子、苹果和番茄作为目标域数据集。结果表明，目标数据集的标注平均精度分别达到了90.94%、89.78%和90.84%。EasyDAM_V3模型可以在自动标注任务中获得最优源域，从而消除人工标注过程，降低相关成本和人力。
