Journal of Convergence for Information Technology (융합정보논문지)

Convergence Society for SMB (중소기업융합학회)
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Contact Detection based on Relative Distance Prediction using Deep Learning-based Object Detection

딥러닝 기반의 객체 검출을 이용한 상대적 거리 예측 및 접촉 감지

  • Hong, Seok-Mi (Department of Liberal Arts, Sangji University) ;
  • Sun, Kyunghee (Contents Convergence Software Research Institute, Kyonggi University) ;
  • Yoo, Hyun (Contents Convergence Software Research Institute, Kyonggi University)
  • 홍석미 (상지대학교 교양대학) ;
  • 선경희 (경기대학교 콘텐츠융합소프트웨어연구소) ;
  • 유현 (경기대학교 콘텐츠융합소프트웨어연구소)
  • Received : 2021.11.01
  • Accepted : 2022.01.20
  • Published : 2022.01.28
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Abstract

The purpose of this study is to extract the type, location, and absolute size of an object in an image using a deep learning algorithm, predict the relative distance between objects, and use this to detect contact between objects. To analyze the size ratio of objects, YOLO, a CNN-based object detection algorithm, is used. Through the YOLO algorithm, the absolute size and position of an object are extracted in the form of coordinates. The extraction result extracts the ratio between the size in the image and the actual size from the standard object-size list having the same object name and size stored in advance, and predicts the relative distance between the camera and the object in the image. Based on the predicted value, it detects whether the objects are in contact.

본 연구의 목적은 딥러닝 알고리즘을 이용하여 영상 내 객체의 종류, 위치, 절대 크기를 추출하고, 객체간 상대적 거리를 예측하며, 이를 이용하여 객체 간의 접촉을 감지하기 위한 내용이다. 객체의 크기 비율을 분석하기 위하여, CNN 기반의 Object Detection 알고리즘인 YOLO를 이용한다. YOLO 알고리즘을 통하여 2D 형태의 이미지에서 각 개체의 절대적인 크기와 위치를 좌표의 형태로 추출한다. 추출 결과는 사전에 저장된 동일한 객체의 명칭과 크기를 가지는 표준 객체-크기 리스트로부터 영상 내 크기와 실제 크기 간의 비례를 추출하며, 영상 내 카메라-객체 간의 상대적인 거리를 예측한다. 예측된 값을 바탕으로 영상에서 객체 간 접촉 여부를 감지한다.

Keywords

Acknowledgement

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant No.: NRF-2020R1A6A1A03040583).

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