论文标题
通过深度学习从数字化的标志扫描中检测和注释植物器官
Detection and Annotation of Plant Organs from Digitized Herbarium Scans using Deep Learning
论文作者
论文摘要
随着在在线存储库中越来越多地将标本室标本数字化和访问，因此正在使用先进的计算机视觉技术来从中提取信息。在标本室床单上存在某些植物器官是各种科学环境中有用的信息，并且自动识别这些器官将有助于动员此类信息。在我们的研究中，我们使用深度学习来检测具有更快的R-CNN的数字化标本室标本上的植物器官。在我们的实验中，我们用数千种六种类型的植物器官的框架手动注释了数百份标本式扫描，并将其用于训练和评估植物器官检测模型。该模型在叶子和茎上效果特别好，而花朵也有大量的花朵出现，但并不认可。
As herbarium specimens are increasingly becoming digitized and accessible in online repositories, advanced computer vision techniques are being used to extract information from them. The presence of certain plant organs on herbarium sheets is useful information in various scientific contexts and automatic recognition of these organs will help mobilize such information. In our study we use deep learning to detect plant organs on digitized herbarium specimens with Faster R-CNN. For our experiment we manually annotated hundreds of herbarium scans with thousands of bounding boxes for six types of plant organs and used them for training and evaluating the plant organ detection model. The model worked particularly well on leaves and stems, while flowers were also present in large numbers in the sheets, but not equally well recognized.
