Small object detection (SOD) system for comprehensive construction site safety monitoring

Siyeon Kim, Seok Hwan Hong, Hyodong Kim, Meesung Lee, Sungjoo Hwang

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number105103
JournalAutomation in Construction
Volume156
DOIs
StatePublished - Dec 2023

Bibliographical note

Publisher Copyright:
© 2023 Elsevier B.V.

Keywords

  • Computer vision
  • Deep learning
  • Edge computing
  • Safety management
  • Site monitoring
  • Small object detection

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