论文标题
通过深度学习水果和其他圆形物体的深度学习中心对象进行分割
Central object segmentation by deep learning for fruits and other roundish objects
论文作者
论文摘要
我们提出农作物（中央圆形对象画家），该作物在RGB图像的中心识别并绘制对象。作物主要用于各种照明条件下的圆形水果，但令人惊讶的是，它也可以处理其他有机或无机材料的图像，或者通过光学和电子显微镜的图像处理，尽管作物仅受172张水果图像进行了培训。该方法涉及通过深度学习的图像进行分割，而神经网络的体系结构是原始U-NET的更深版本。这项技术可以为我们提供一种自动收集农场水果增长的统计数据的方法。例如，我们描述了处理510个时间序列照片的实验，以自动收集有关目标水果的大小和位置的数据。我们训练有素的神经网络作物和上述自动程序可在GitHub上提供用户友好的接口程序。
We present CROP (Central Roundish Object Painter), which identifies and paints the object at the center of an RGB image. Primarily CROP works for roundish fruits in various illumination conditions, but surprisingly, it could also deal with images of other organic or inorganic materials, or ones by optical and electron microscopes, although CROP was trained solely by 172 images of fruits. The method involves image segmentation by deep learning, and the architecture of the neural network is a deeper version of the original U-Net. This technique could provide us with a means of automatically collecting statistical data of fruit growth in farms. As an example, we describe our experiment of processing 510 time series photos automatically to collect the data on the size and the position of the target fruit. Our trained neural network CROP and the above automatic programs are available on GitHub with user-friendly interface programs.
