Object Detection Using MASK R-CNN

• Author(s): K. Maruthi Pavan Surya ; M. Padmasri Balasubrahmanyam ; N. Kumar Venkata Siva ; M. Venkata Sivanjaneyulu
• Paper ID: 1702129
• Page: 18-22
• Published Date: 02-04-2020
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 3 Issue 10 April-2020

Abstract

In today's world, Automation takes the front seat of growth, and one of the important things for that machine vision. We need good models for better results for computer vision. Here we present a conceptually clear, versatile, and general structure for segmentation of object instances. Our model detects objects in an image more efficiently and at the same time it generates a high quality segmentation mask for each object in the image. The model is called Mask R-CNN it actually extends Faster R-CNN model by adding a branch to predict an object mask in parallel with the bounding box recognition branch. Mask R-CNN is quick to train and adds a slight overhead to Faster R-CNN which runs at 5 fps. In addition, Mask R-CNN is easy to generalize for other functions, e.g. allowing us to estimate human poses within the same framework.

Keywords

CNN (Convolution Neural Network), ROI (Region of Interest), Instance Segmentation, Region Proposal Nertwork (RPN)

Citations

IRE Journals:
K. Maruthi Pavan Surya , M. Padmasri Balasubrahmanyam , N. Kumar Venkata Siva , M. Venkata Sivanjaneyulu "Object Detection Using MASK R-CNN" Iconic Research And Engineering Journals Volume 3 Issue 10 2020 Page 18-22

IEEE:
K. Maruthi Pavan Surya , M. Padmasri Balasubrahmanyam , N. Kumar Venkata Siva , M. Venkata Sivanjaneyulu "Object Detection Using MASK R-CNN" Iconic Research And Engineering Journals, 3(10)