A Voxel-Based 3D reconstruction and action recognition method for construction workers

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics Pub Date : 2025-02-14 DOI:10.1016/j.aei.2025.103203

Jin Zhang, Daoming Wang, Xuehui An, Miao Lv, Dexing Chen, Aoran Sun

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引用次数: 0

Abstract

Workers are critical yet unpredictable elements on construction sites, with their actions significantly impacting safety and productivity. Recognizing these actions is essential for improving efficiency, safety, and quality. Benefiting from the advantages of the voxel format in terms of universal representation, privacy protection and memory saving, a voxel-based method for action recognition was proposed. By transforming the image into a structured voxel, a lightweight 3D CNN, CVARnet (Construction worker Voxel Action Recognition network) was established. To verify the effectiveness of voxel and CVARnet, a dataset named Construction Action Voxel Classification (CAVC) was developed. The image was primarily sourced from the construction site and represented five types of typical actions. The proposed CVARnet achieved 86% ACC in a classification task, demonstrating efficient recognition capabilities for workers’ actions. This study presented a novel perspective with a voxel format, providing innovative insight for the action recognition task.

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来源期刊

Advanced Engineering Informatics 工程技术-工程：综合

CiteScore

12.40

自引率

18.20%

发文量

292

审稿时长

45 days

期刊介绍： Advanced Engineering Informatics is an international Journal that solicits research papers with an emphasis on 'knowledge' and 'engineering applications'. The Journal seeks original papers that report progress in applying methods of engineering informatics. These papers should have engineering relevance and help provide a scientific base for more reliable, spontaneous, and creative engineering decision-making. Additionally, papers should demonstrate the science of supporting knowledge-intensive engineering tasks and validate the generality, power, and scalability of new methods through rigorous evaluation, preferably both qualitatively and quantitatively. Abstracting and indexing for Advanced Engineering Informatics include Science Citation Index Expanded, Scopus and INSPEC.