The Journal of Korea Robotics Society (로봇학회논문지)

Korea Robotics Society (한국로봇학회)
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Small Marker Detection with Attention Model in Robotic Applications

로봇시스템에서 작은 마커 인식을 하기 위한 사물 감지 어텐션 모델

  • Kim, Minjae (Mechanical Engineering, Sungkyunkwan University) ;
  • Moon, Hyungpil (Mechanical Engineering, Sungkyunkwan University)
  • Received : 2022.09.07
  • Accepted : 2022.10.31
  • Published : 2022.11.30
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Abstract

As robots are considered one of the mainstream digital transformations, robots with machine vision becomes a main area of study providing the ability to check what robots watch and make decisions based on it. However, it is difficult to find a small object in the image mainly due to the flaw of the most of visual recognition networks. Because visual recognition networks are mostly convolution neural network which usually consider local features. So, we make a model considering not only local feature, but also global feature. In this paper, we propose a detection method of a small marker on the object using deep learning and an algorithm that considers global features by combining Transformer's self-attention technique with a convolutional neural network. We suggest a self-attention model with new definition of Query, Key and Value for model to learn global feature and simplified equation by getting rid of position vector and classification token which cause the model to be heavy and slow. Finally, we show that our model achieves higher mAP than state of the art model YOLOr.

Keywords

Acknowledgement

This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2022-2020-0-01460) supervised by the IITP (Institute of Information & Communications Technology Planning & Evaluation

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