Comprative Study and Analysis of Object Detection using R-CNN
Sneha Madane1, Shruti Patil2, Rohini Patil3
1Sneha Madane, Department of Computer Science, Terna Engineering College, Navi Mumbai (Maharashtra)-400706, India.
2Shruti Patil, Department of Computer Science, Terna Engineering College, Navi Mumbai (Maharashtra)-400706, India.
3Rohini Patil, Department of Computer Science, Terna Engineering College, Navi Mumbai (Maharashtra)-400706, India.
Manuscript received on May 02, 2019. | Revised Manuscript received on May 05, 2019. | Manuscript published on May 30, 2019. | PP: 23-26 | VVolume-9 Issue-1, May 2019. | Retrieval Number: A3214059119/19©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Object detection is a field that has been in the limelight a lot in the recent years. Computer vision and image processing are involved in this computer technology and are widely used. Along the path of harnessing the power of vision, numerous algorithms have been found from simple edge detection to pixel level object detection. In this paper we have studied the advancements in object detection algorithms like RCNN and the latest one being we have studied papers based on types of R-CNN like Fast R-CNN, Faster R-CNN and Mask RCNN. We have seen their applications in various fields, studied their efficiency, accuracy and limitations.
Keywords: Region based Convolutional Neural Networks (RCNN), Object Detection