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공과대학
건축도시시스템공학과
Journal papers
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Small object detection (SOD) system for comprehensive construction site safety monitoring
Title
Small object detection (SOD) system for comprehensive construction site safety monitoring
Authors
Kim
;
Siyeon
;
Hong
;
Seok Hwan
;
Hyodong
;
Lee
;
Meesung
;
Hwang
;
Sungjoo
Ewha Authors
황성주
SCOPUS Author ID
황성주
Issue Date
2023
Journal Title
Automation in Construction
ISSN
0926-5805
Citation
Automation in Construction vol. 156
Keywords
Computer vision
;
Deep learning
;
Edge computing
;
Safety management
;
Site monitoring
;
Small object detection
Publisher
Elsevier B.V.
Indexed
SCIE; SCOPUS
Document Type
Article
Abstract
Although object detection is essential for recognizing hazardous situations in construction sites where various objects coexist, existing systems fail to ensure real-time accuracy and flexibility in detecting small objects in various scene scales. Therefore, a small object detection (SOD) system was developed based on the YOLOv5 algorithm for comprehensive site monitoring. The proposed SOD simultaneously crops images into multiple segments for small object detection set by the user's desired flexibility while gaining real-time inference in edge computing environments. The SOD outperforms existing systems, especially regarding small object detection accuracy and flexibility for detecting objects of different sizes. The SOD can detect multi-scale objects not initially detected by existing methods (i.e., workers) to large construction equipment without much inference time lost in the edge device. The proposed system facilitates real-time site monitoring by correcting existing system limitations, thereby improving site monitoring and safety management. © 2023 Elsevier B.V.
DOI
10.1016/j.autcon.2023.105103
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