Faster R-CNN for Object Location in a Virtual Environment for Sorting Task

Javier O. Pinzón Arenas, Robinson Jiménez, Paula C. Useche Murillo


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.


Faster R-CNN, Object Recognition, Virtual Environment, Autonomous Mobile Agent, Region of Interest.

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International Journal of Online and Biomedical Engineering (iJOE) – eISSN: 2626-8493
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