Central object segmentation by deep learning for fruits and other roundish objects
This provides an incremental solution for automating statistical data collection on fruit growth in farms, with potential applications to other roundish objects.
The paper tackles the problem of segmenting central roundish objects in images, primarily for fruits, by introducing CROP, a deep learning method based on a deeper U-Net, which achieved segmentation using only 172 training images and processed 510 time series photos automatically.
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.