The computer vision has rapidly upgraded in area of object detection, localization and segmentation over short period of time. How fast and consistent has become a key question in this vision community. With great increased in techniques, the accuracy for object detection has improved significantly. While performing such enormous task a systematic search is needed so as not to miss out any object as far as possible. This however required extensive efforts and time. In this paper we show that Mask R-CNN can be deployed in this field. Mask RCNN is a recently propose state-of-art algorithm. It is a simple and wide-ranging framework for bounding-box object detection and segmentation of images with a mask. Mask R-CNN is an extension of Fast R-CNN by adding a prediction of each objects in an image along with the existing method of bounding box recognition. The proposed method shows higher accuracy in detecting objects.
Volume 12 | 03-Special Issue
Pages: 95-104
DOI: 10.5373/JARDCS/V12SP3/20201242