  Quantitative measurement of crystals in high-resolution images allows for
important insights into underlying material characteristics. Deep learning has
shown great progress in vision-based automatic crystal size measurement, but
current instance segmentation methods reach their limits with images that have
large variation in crystal size or hard to detect crystal boundaries. Even
small image segmentation errors, such as incorrectly fused or separated
segments, can significantly lower the accuracy of the measured results. Instead
of improving the existing pixel-wise boundary segmentation methods, we propose
to use an instance-based segmentation method, which gives more robust
segmentation results to improve measurement accuracy. Our novel method enhances
flow maps with a size-aware multi-scale attention module. The attention module
adaptively fuses information from multiple scales and focuses on the most
relevant scale for each segmented image area. We demonstrate that our proposed
attention fusion strategy outperforms state-of-the-art instance and boundary
segmentation methods, as well as simple average fusion of multi-scale
predictions. We evaluate our method on a refractory raw material dataset of
high-resolution images with large variation in crystal size and show that our
model can be used to calculate the crystal size more accurately than existing
methods.
