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http://dx.doi.org/10.7838/jsebs.2022.27.2.205

Automatic Collection of Production Performance Data Based on Multi-Object Tracking Algorithms  

Lim, Hyuna (Department of Industrial and Systems Engineering, Kyonggi University)
Oh, Seojeong (Department of Industrial and Systems Engineering, Kyonggi University)
Son, Hyeongjun (Department of Industrial and Systems Engineering, Kyonggi University)
Oh, Yosep (Department of Industrial and Systems Engineering, Kyonggi University)
Publication Information
The Journal of Society for e-Business Studies / v.27, no.2, 2022 , pp. 205-218 More about this Journal
Abstract
Recently, digital transformation in manufacturing has been accelerating. It results in that the data collection technologies from the shop-floor is becoming important. These approaches focus primarily on obtaining specific manufacturing data using various sensors and communication technologies. In order to expand the channel of field data collection, this study proposes a method to automatically collect manufacturing data based on vision-based artificial intelligence. This is to analyze real-time image information with the object detection and tracking technologies and to obtain manufacturing data. The research team collects object motion information for each frame by applying YOLO (You Only Look Once) and DeepSORT as object detection and tracking algorithms. Thereafter, the motion information is converted into two pieces of manufacturing data (production performance and time) through post-processing. A dynamically moving factory model is created to obtain training data for deep learning. In addition, operating scenarios are proposed to reproduce the shop-floor situation in the real world. The operating scenario assumes a flow-shop consisting of six facilities. As a result of collecting manufacturing data according to the operating scenarios, the accuracy was 96.3%.
Keywords
Artificial Intelligence; Multi-Object Tracking; Manufacturing Data Collection; Flow Shop;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
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