Development of an action classification method for construction sites combining pose assessment and object proximity evaluation

3区 计算机科学 Q1 Computer Science Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-03-05 DOI:10.1007/s12652-024-04753-7
Toshiya Kikuta, Pang-jo Chun
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Abstract

Addressing the inherent hazards of on-site construction work and stagnant labor productivity is crucial in the construction industry. To tackle these challenges, automated monitoring of construction sites and analysis of workers' actions play a pivotal role. In this study, we developed a method for classifying actions at a construction site from video, using deep learning. Specifically, we used two image processing techniques, pose assessment and object detection, and found that the accuracy of action classification was improved by extracting information on the proximity of workers to equipment installed at the construction site, and also by considering the pose information. For classification, LSTM (Long Short-Term Memory), CNN (Convolutional Neural Network), and XGBoost models were used, and the presence of proximity information improved average recall by 7.0% to 8.5% for all models used. The final model was developed as an ensemble of these methods, offering accuracy and average recall that are higher than with conventional methods. The methodology developed in this research enables quantification and visualization of work content at construction sites, contributing to the overall enhancement of safety and productivity within the construction industry.

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结合姿势评估和物体接近度评估,开发建筑工地行动分类方法
解决现场施工作业固有的危险和劳动生产率停滞不前的问题对建筑行业至关重要。为了应对这些挑战,建筑工地的自动监控和对工人行为的分析起着至关重要的作用。在本研究中,我们开发了一种利用深度学习从视频中对建筑工地上的行为进行分类的方法。具体来说,我们使用了姿势评估和物体检测两种图像处理技术,并发现通过提取工人与建筑工地上安装的设备的距离信息,以及考虑姿势信息,可以提高动作分类的准确性。在分类过程中,使用了 LSTM(长短期记忆)、CNN(卷积神经网络)和 XGBoost 模型,在所有使用的模型中,邻近信息的存在将平均召回率提高了 7.0% 至 8.5%。最终模型是作为这些方法的集合而开发的,其准确性和平均召回率均高于传统方法。本研究开发的方法可实现建筑工地工作内容的量化和可视化,有助于全面提高建筑行业的安全性和生产率。
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来源期刊
Journal of Ambient Intelligence and Humanized Computing
Journal of Ambient Intelligence and Humanized Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.60
自引率
0.00%
发文量
854
期刊介绍: The purpose of JAIHC is to provide a high profile, leading edge forum for academics, industrial professionals, educators and policy makers involved in the field to contribute, to disseminate the most innovative researches and developments of all aspects of ambient intelligence and humanized computing, such as intelligent/smart objects, environments/spaces, and systems. The journal discusses various technical, safety, personal, social, physical, political, artistic and economic issues. The research topics covered by the journal are (but not limited to): Pervasive/Ubiquitous Computing and Applications Cognitive wireless sensor network Embedded Systems and Software Mobile Computing and Wireless Communications Next Generation Multimedia Systems Security, Privacy and Trust Service and Semantic Computing Advanced Networking Architectures Dependable, Reliable and Autonomic Computing Embedded Smart Agents Context awareness, social sensing and inference Multi modal interaction design Ergonomics and product prototyping Intelligent and self-organizing transportation networks & services Healthcare Systems Virtual Humans & Virtual Worlds Wearables sensors and actuators
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