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

Authors

DOI:

https://doi.org/10.3991/ijoe.v14i07.8465

Keywords:

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.

Author Biography

Robinson Jiménez, Militar Nueva Granada University

Bogota

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Published

2018-07-27

How to Cite

Pinzón Arenas, J. O., Jiménez, R., & Useche Murillo, P. C. (2018). Faster R-CNN for Object Location in a Virtual Environment for Sorting Task. International Journal of Online and Biomedical Engineering (iJOE), 14(07), pp. 4–14. https://doi.org/10.3991/ijoe.v14i07.8465

Issue

Section

Papers