Neural network for identification of danger situation using stereovision

Authors:
Andrzej Grabowski;Robert Kosinski
Affiliations:
Central Institute for Labour Protection, National Research Institute, Department of Safety Engineering, Warsaw, Poland;Central Institute for Labour Protection, National Research Institute, Department of Safety Engineering, Warsaw and Warsaw University of Technology, Faculty of Physics, Warsaw, Poland
Venue:
ICOSSSE'08 Proceedings of the 7th WSEAS international conference on System science and simulation in engineering
Year:
2008

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Abstract

Modern production processes becomes more and more flexible. Therefore there is a need that devices used in workplace also support flexibility as much as possible. Such characteristics have Vision Based Protective Devices (VBPDs). We present a neural system for the advanced recognition of danger situation for safety control. The sequence of the images from two cameras located above the robot is presented to the system of cellular neural networks (CNNs) realized in the PC computer. They detect a new object appearing in a Safety Field (SF), define its position with respect to the robot arm and perform the feature extraction of its image. Experiments conducted using artificial images (virtual environment) and low quality images (internet cameras) indicate that our system can work in a real time and detect successively dangerous situations.