3D convolutional neural network-based one-stage model for real-time action detection in video of construction equipment

Seunghoon Jung, Jaewon Jeoung, Hyuna Kang, Taehoon Hong

Research output: Contribution to journalArticlepeer-review

Abstract

This study aims to propose a three-dimensional convolutional neural network (3D CNN)-based one-stage model for real-time action detection in video of construction equipment (ADVICE). The 3D CNN-based single-stream feature extraction network and detection network are designed with the implementation of the 3D attention module and feature pyramid network developed in this study to improve performance. For model evaluation, 130 videos were collected from YouTube including videos of four types of construction equipment at various construction sites. Trained on 520 clips and tested on 260 clips, ADVICE achieved precision and recall of 82.1% and 83.1%, respectively, with an inference speed of 36.6 frames per second. The evaluation results indicate that the proposed method can implement the 3D CNN-based one-stage model for real-time action detection of construction equipment in videos of diverse, variable, and complex construction sites. The proposed method paved the way to improving safety, productivity, and environmental management of construction projects.

Original languageEnglish
JournalComputer-Aided Civil and Infrastructure Engineering
DOIs
Publication statusAccepted/In press - 2021

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT; No. NRF‐2018R1A5A1025137).

Funding Information:
National Research Foundation of Korea, Grant/Award Number: NRF‐2018R1A5A1025137

Publisher Copyright:
© 2021 Computer-Aided Civil and Infrastructure Engineering

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design
  • Computational Theory and Mathematics

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