Faster R-CNN for Object Location in a Virtual Environment for Sorting Task
DOI:
https://doi.org/10.3991/ijoe.v14i07.8465Keywords:
Faster R-CNN, Object Recognition, Virtual Environment, Autonomous Mobile Agent, Region of Interest.Abstract
This paper presents the implementation of a mobile robotic arm simulation whose task is to order different objects randomly distributed in a workspace. To develop this task, it is used a Faster R-CNN which is going to identify and locate the disordered elements, reaching 99% accuracy in validation tests and 100% in real-time tests, i.e. the robot was able to collect and locate all the objects to be ordered, taking into account that the virtual environment is controlled and the size of the input image obtained from the workspace to be entered to the network should be 700x525 px.
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