التفاصيل البيبلوغرافية
| العنوان: |
제조업 근로자 안전관리를 위한 데이터셋 구축과 모델 학습. (Korean) |
| Alternate Title: |
Dataset Construction and Model Learning for Manufacturing Worker Safety Management. (English) |
| المؤلفون: |
이태준, 김윤정, 정회경 |
| المصدر: |
Journal of the Korea Institute of Information & Communication Engineering; Jul2021, Vol. 25 Issue 7, p890-895, 6p |
| مصطلحات موضوعية: |
OBJECT recognition (Computer vision), DEEP learning, INDUSTRIAL safety, STATISTICS, GOVERNMENT agencies, DISASTERS |
| مستخلص: |
Recently, the “Act of Serious Disasters, etc” was enacted and institutional and social interest in safety accidents is increasing. In this paper, we analyze statistical data published by government agency on safety accidents that occur in manufacturing sites, and compare various object detection models based on deep learning to build a model to determine dangerous situations to reduce the occurrence of safety accidents. The data-set was directly constructed by collecting images from CCTVs at the manufacturing site, and the YOLO-v4, SSD, CenterNet models were used as training data and evaluation data for learning. As a result, the YOLO-v4 model obtained a value of 81% of mAP. It is meaningful to select a class in an industrial field and directly build a dataset to learn a model, and it is thought that it can be used as an initial research data for a system that determines a risk situation and infers it. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
