Small object detection (SOD) system for comprehensive construction site safety monitoring
- 주제(키워드) Computer vision , Deep learning , Edge computing , Safety management , Site monitoring , Small object detection
- 등재 SCOPUS
- 발행기관 Elsevier B.V.
- 발행년도 2023
- URI http://www.dcollection.net/handler/ewha/000000211865
- 본문언어 영어
- Published As https://doi.org/10.1016/j.autcon.2023.105103
초록/요약
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.
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