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Vision safety system based on cellular neural networks

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Abstract

This work is dedicated to develop a safety measurement for human–machine cooperative system, in which the machine region and the human region cannot be separated due to the overlap and the movement both from human and from machines. Modern production processes become 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. 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, 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. We have found that the CNN is unable to detect a new object properly in the presence of high level of noise as a result of percolation type phase transition. An example of possible application of the system is presented.

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Authors and Affiliations

  1. Central Institute for Labour Protection, National Research Institute, Warsaw, Poland

    A. Grabowski, R. A. Kosiński & M. Dźwiarek

  2. Faculty of Physics, Warsaw University of Technology, Warsaw, Poland

    R. A. Kosiński

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  1. A. Grabowski
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  2. R. A. Kosiński
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  3. M. Dźwiarek
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Correspondence to A. Grabowski.

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Grabowski, A., Kosiński, R.A. & Dźwiarek, M. Vision safety system based on cellular neural networks. Machine Vision and Applications 22, 581–590 (2011). https://doi.org/10.1007/s00138-010-0269-9

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  • DOI: https://doi.org/10.1007/s00138-010-0269-9

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