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http://dx.doi.org/10.23087/jkicsp.2022.23.3.006

Implementation of an alarm system with AI image processing to detect whether a helmet is worn or not and a fall accident  

Yong-Hwa Jo (Department of Information & Communication AI Engineering, Kyungnam University)
Hyuek-Jae Lee (Department of Information & Communication AI Engineering, Kyungnam University)
Publication Information
Journal of the Institute of Convergence Signal Processing / v.23, no.3, 2022 , pp. 150-159 More about this Journal
Abstract
This paper presents an implementation of detecting whether a helmet is worn and there is a fall accident through individual image analysis in real-time from extracting the image objects of several workers active in the industrial field. In order to detect image objects of workers, YOLO, a deep learning-based computer vision model, was used, and for whether a helmet is worn or not, the extracted images with 5,000 different helmet learning data images were applied. For whether a fall accident occurred, the position of the head was checked using the Pose real-time body tracking algorithm of Mediapipe, and the movement speed was calculated to determine whether the person fell. In addition, to give reliability to the result of a falling accident, a method to infer the posture of an object by obtaining the size of YOLO's bounding box was proposed and implemented. Finally, Telegram API Bot and Firebase DB server were implemented for notification service to administrators.
Keywords
Convolutional Neural Network (CNN); YOLO; Object detection; Deep learning; Industrial site;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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