Journal of Sensor Science and Technology (센서학회지)

The Korean Sensors Society (한국센서학회)
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Deep Learning Machine Vision System with High Object Recognition Rate using Multiple-Exposure Image Sensing Method

  • Park, Min-Jun (School of Electrical Information Communication Engineering, Kangwon National Unversity) ;
  • Kim, Hyeon-June (School of Electrical Information Communication Engineering, Kangwon National Unversity)
  • Received : 2021.03.17
  • Accepted : 2021.03.29
  • Published : 2021.03.31
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Abstract

In this study, we propose a machine vision system with a high object recognition rate. By utilizing a multiple-exposure image sensing technique, the proposed deep learning-based machine vision system can cover a wide light intensity range without further learning processes on the various light intensity range. If the proposed machine vision system fails to recognize object features, the system operates in a multiple-exposure sensing mode and detects the target object that is blocked in the near dark or bright region. Furthermore, short- and long-exposure images from the multiple-exposure sensing mode are synthesized to obtain accurate object feature information. That results in the generation of a wide dynamic range of image information. Even with the object recognition resources for the deep learning process with a light intensity range of only 23 dB, the prototype machine vision system with the multiple-exposure imaging method demonstrated an object recognition performance with a light intensity range of up to 96 dB.

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