  We present the system that we have developed for the identification and
verification of pills using images that are taken by the VeriMedi device. The
VeriMedi device is an Internet of Things device that takes pictures of a filled
pill vial from the bottom of the vial and uses the solution that is presented
in this research to identify the pills in the vials. The solution has two
serially connected deep learning solutions which do segmentation and
identification. The segmentation solution creates the masks for each pill in
the vial image by using the Mask R-CNN model, then segments and crops the pills
and blurs the background. After that, the segmented pill images are sent to the
identification solution where a Deep Metric Learning model that is trained with
Proxy Anchor Loss (PAL) function generates embedding vectors for each pill
image. The generated embedding vectors are fed into a one-layer fully connected
network that is trained with the exact solution to predict each single pill
image. Then, the aggregation/verification function aggregates the multiple
predictions coming from multiple single pill images and verifies the
correctness of the final prediction with respect to predefined rules. Besides,
we enhanced the PAL with a better proxy initialization that increased the
performance of the models and let the model learn the new classes of images
continually without retraining the model with the whole dataset. When the model
that is trained with initial classes is retrained only with new classes, the
accuracy of the model increases for both old and new classes. The
identification solution that we have presented in this research can also be
reused for other problem domains which require continual learning and/or
Fine-Grained Visual Categorization.
