  Teeth segmentation and recognition are critical in various dental
applications and dental diagnosis. Automatic and accurate segmentation
approaches have been made possible by integrating deep learning models.
Although teeth segmentation has been studied in the past, only some techniques
were able to effectively classify and segment teeth simultaneously. This
article offers a pipeline of two deep learning models, U-Net and YOLOv8, which
results in BB-UNet, a new architecture for the classification and segmentation
of teeth on panoramic X-rays that is efficient and reliable. We have improved
the quality and reliability of teeth segmentation by utilising the YOLOv8 and
U-Net capabilities. The proposed networks have been evaluated using the mean
average precision (mAP) and dice coefficient for YOLOv8 and BB-UNet,
respectively. We have achieved a 3\% increase in mAP score for teeth
classification compared to existing methods, and a 10-15\% increase in dice
coefficient for teeth segmentation compared to U-Net across different
categories of teeth. A new Dental dataset was created based on UFBA-UESC
dataset with Bounding-Box and Polygon annotations of 425 dental panoramic
X-rays. The findings of this research pave the way for a wider adoption of
object detection models in the field of dental diagnosis.
