A synthetic data-enhanced method for automated 3D pose recognition of construction workers

IF 7.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yonglin Fu , Weisheng Lu , Zhiming Dong , Yihai Fang
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引用次数: 0

Abstract

Automated 3D pose recognition of construction workers is instrumental to analyzing their occupational safety and health, productivity and other jobsite behaviors. Existing studies in this field have been confined to high-quality training datasets collected from real-life construction jobsites, potentially triggering ethical, privacy, and cost concerns. Inspired by the success of synthetic data in other fields, this research proposes a synthetic data-enhanced method for automated 3D pose recognition of construction workers. It generates a synthetic dataset to supplement a real-life dataset for model training, presents a monocular vision-based model for recognizing multiple workers’ 3D poses, and then validates the model performance. Experiments verify that this model jointly trained with synthetic and real data outperforms a model trained on real data alone. The data enrichment approach explored in this study offers reliable data quality at less expense than real data-focused approaches. This research therefore lays a foundation for a series of studies to enhance workers’ occupational safety and health and productivity.
一种综合数据增强的建筑工人三维姿态自动识别方法
建筑工人的自动3D姿势识别有助于分析他们的职业安全和健康、生产力和其他工地行为。该领域的现有研究仅限于从现实建筑工地收集的高质量训练数据集,可能引发道德、隐私和成本问题。受合成数据在其他领域成功应用的启发,本研究提出了一种基于合成数据增强的建筑工人三维姿态自动识别方法。它生成一个合成数据集来补充真实数据集用于模型训练,提出了一个基于单目视觉的模型来识别多个工人的3D姿势,然后验证了模型的性能。实验证明,该模型与合成数据和真实数据联合训练的效果优于单独使用真实数据训练的模型。本研究中探索的数据丰富方法比真正的以数据为中心的方法以更低的成本提供可靠的数据质量。因此,本研究为加强工人的职业安全、健康和生产力的一系列研究奠定了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Expert Systems with Applications
Expert Systems with Applications 工程技术-工程:电子与电气
CiteScore
13.80
自引率
10.60%
发文量
2045
审稿时长
8.7 months
期刊介绍: Expert Systems With Applications is an international journal dedicated to the exchange of information on expert and intelligent systems used globally in industry, government, and universities. The journal emphasizes original papers covering the design, development, testing, implementation, and management of these systems, offering practical guidelines. It spans various sectors such as finance, engineering, marketing, law, project management, information management, medicine, and more. The journal also welcomes papers on multi-agent systems, knowledge management, neural networks, knowledge discovery, data mining, and other related areas, excluding applications to military/defense systems.
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