Institute for Systems and Robotics (ISR), Instituto Superior Técnico (IST), University of Lisbon, Lisbon, Portugal.
Biological Sciences, National University of Singapore, Singapore, Singapore.
PLoS One. 2023 Feb 13;18(2):e0280998. doi: 10.1371/journal.pone.0280998. eCollection 2023.
Butterflies are increasingly becoming model insects where basic questions surrounding the diversity of their color patterns are being investigated. Some of these color patterns consist of simple spots and eyespots. To accelerate the pace of research surrounding these discrete and circular pattern elements we trained distinct convolutional neural networks (CNNs) for detection and measurement of butterfly spots and eyespots on digital images of butterfly wings. We compared the automatically detected and segmented spot/eyespot areas with those manually annotated. These methods were able to identify and distinguish marginal eyespots from spots, as well as distinguish these patterns from less symmetrical patches of color. In addition, the measurements of an eyespot's central area and surrounding rings were comparable with the manual measurements. These CNNs offer improvements of eyespot/spot detection and measurements relative to previous methods because it is not necessary to mathematically define the feature of interest. All that is needed is to point out the images that have those features to train the CNN.
蝴蝶正逐渐成为模型昆虫，人们正在研究其颜色图案多样性的基本问题。其中一些颜色图案由简单的斑点和眼斑组成。为了加快研究这些离散和圆形图案元素的步伐，我们针对蝴蝶翅膀数字图像上的蝴蝶斑点和眼斑检测和测量，训练了不同的卷积神经网络 (CNN)。我们将自动检测和分割的斑点/眼斑区域与手动注释进行了比较。这些方法能够识别和区分边缘眼斑和斑点，并将这些图案与颜色不太对称的斑块区分开来。此外，眼斑中央区域和周围环的测量值与手动测量值相当。与以前的方法相比，这些 CNN 在眼斑/斑点检测和测量方面有了改进，因为不需要对感兴趣的特征进行数学定义。只需要指出具有这些特征的图像，即可对 CNN 进行训练。
