Deep Learning Based Hand Wrist Segmentation using Mask R-CNN
|
GokulaKrishnan Elumalai School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India This email address is being protected from spambots. You need JavaScript enabled to view it. |
Malathi Ganesan School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India This email address is being protected from spambots. You need JavaScript enabled to view it. |
Abstract: Deep learning is one of the trending technologies in computer vision to identify and classify objects. Deep learning is a subset of Machine Learning and Artificial Intelligence. Detecting and classifying the object was a challenging task in traditional computer vision techniques, and now there are numerous deep learning techniques scaled up to achieve this. The primary purpose of the research is to detect and segment the human hand wrist region using deep learning methods. This research is widespread to deep learning enthusiasts who needs to segment custom objects using instance segmentation. We demonstrated a segmented hand wrist using the Mask Regional Convolutional Neural Network (R-CNN) technique with an average accuracy of 99.73%. This work also compares the performance evaluation of baseline and custom Hand Wrist Mask R-CNN. The achieved validation class loss is 0.00866 training and 0.02736 validation; both the values are comparatively deficient compared with baseline Mask R-CNN.
Keywords: Hand wrist, segmentation, mask R-CNN, fast R-CNN, faster R-CNN, object detection.
Received June 27, 2020; accepted October 13, 2021