Object Recognition for Dental Instruments Using SSD-MobileNet

Abstract

In recent technological developments, robot-assisted surgery has become popular due to its tremendous prospects in enhancing the capabilities of surgeons performing open surgery, yet very little effort has been made to make these tools available to dental surgeons. This paper addresses the problem of identifying the problem of real-time object recognition of dental instruments by utilizing deep learning techniques. For this reason, the Single Shot MultiBox Detector (SSD) network was considered as the meta structure and joined with the base Convolutional Neural Network (CNN) MobileNet to shape SSD-MobileNet. The task of object recognition for dental instruments like spatula, elevator, mouth mirror etc is performed, in order to constitute a robotic arm; that works with voice commands using speech recognition, and assists the dentist in surgery. Our method can recognize instruments more precisely and quickly as contrast with other lightweight system strategies and conventional machine learning techniques. We have achieved the precision and accuracy of 87.3% and 98.8% respectively.