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http://dx.doi.org/10.22156/CS4SMB.2022.12.01.039

Contact Detection based on Relative Distance Prediction using Deep Learning-based Object Detection  

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)
Publication Information
Journal of Convergence for Information Technology / v.12, no.1, 2022 , pp. 39-44 More about this Journal
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.
Keywords
Image Analysis; Artificial Neural Network; Deep Learning; Contact Detection; YOLO;
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Times Cited By KSCI : 1  (Citation Analysis)
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