  Dendrite core is the center point of the dendrite. The information of
dendrite core is very helpful for material scientists to analyze the properties
of materials. Therefore, detecting the dendrite core is a very important task
in the material science field. Meanwhile, because of some special properties of
the dendrites, this task is also very challenging. Different from the typical
detection problems in the computer vision field, detecting the dendrite core
aims to detect a single point location instead of the bounding-box. As a
result, the existing regressing bounding-box based detection methods can not
work well on this task because the calculated center point location based on
the upper-left and lower-right corners of the bounding-box is usually not
precise. In this work, we formulate the dendrite core detection problem as a
segmentation task and proposed a novel detection method to detect the dendrite
core directly. Our whole pipeline contains three steps: Easy Sample Detection
(ESD), Hard Sample Detection(HSD), and Hard Sample Refinement (HSR).
Specifically, ESD and HSD focus on the easy samples and hard samples of
dendrite cores respectively. Both of them employ the same Central Point
Detection Network (CPDN) but do not share parameters. To make HSD only focus on
the feature of hard samples of dendrite cores, we destroy the structure of the
easy samples of dendrites which are detected by ESD and force HSD to learn the
feature of hard samples. HSR is a binary classifier which is used to filter out
the false positive prediction of HSD. We evaluate our method on the dendrite
dataset. Our method outperforms the state-of-the-art baselines on three
metrics, i.e., Recall, Precision, and F-score.
