Transformer-based deep learning model and video dataset for installation action recognition in offsite projects

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction Pub Date : 2025-02-06 DOI:10.1016/j.autcon.2025.106042

Junyoung Jang , Eunbeen Jeong , Tae Wan Kim

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Abstract

This paper developed and evaluated the Precast Concrete Installation Dataset (PCI-Dataset), a large-scale video dataset for automatically recognizing precast concrete (PC) installation activities. The dataset comprises 12,791 video clips (5 s each, 1080 × 1080 resolution, 30fps) from actual PC construction sites, including 12 balanced activity classes combining three component types and four work stages. Evaluation of six Transformer-based video classification models showed VideoMAE V2 achieved the highest overall accuracy of 98.10 %, followed by UniFormer V2, Video Swin, MVIT, ViViT, and TimeSformer. VideoMAE V2 achieved F1 scores above 80 % for most activities, with a peak of 92.20 % for slab assembly. In a case study on a real PC construction site, the model demonstrated high recognition accuracies: 100 % for lifting, 85.83–100 % for rigging, and 93.75–100 % for assembly operations. The paper contributes to PC construction management theory by applying computer vision for real-time and automated work recognition and analysis.

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来源期刊

Automation in Construction 工程技术-工程：土木

CiteScore

19.20

自引率

16.50%

发文量

563

审稿时长

8.5 months

期刊介绍： Automation in Construction is an international journal that focuses on publishing original research papers related to the use of Information Technologies in various aspects of the construction industry. The journal covers topics such as design, engineering, construction technologies, and the maintenance and management of constructed facilities. The scope of Automation in Construction is extensive and covers all stages of the construction life cycle. This includes initial planning and design, construction of the facility, operation and maintenance, as well as the eventual dismantling and recycling of buildings and engineering structures.