个人防护装备穿戴安全检测技术的进展与应用
Advancements and Applications of Wearable Safety Detection Technology for Personal Protective Equipment
李春亚 1王建华 2尹土兵 3卢建飞 3邹晓波 4叶儒桦5
作者信息
- 1. 武汉理工大学管理学院,武汉 430070;深圳市盐田港置业有限公司,深圳 518000
- 2. 深圳市盐田港置业有限公司,深圳 518000;中南大学资源与安全工程学院,长沙 410083
- 3. 中南大学资源与安全工程学院,长沙 410083
- 4. 深圳市盐田港置业有限公司,深圳 518000
- 5. 深圳港集团有限公司,深圳 518000
- 折叠
摘要
为了分析和总结个人防护装备(PPE)检测技术的发展及其在工地安全管理中的应用,探索传感器和深度学习在检测中的作用与成效,作者采用文献调研与数据分析相结合的方法,基于Citespace系统梳理了近年来PPE检测领域的技术进展.通过比较基于传感器的检测方法与深度学习算法(包括One-stage和Two-stage)的优缺点,全面评估了这些技术在PPE检测中的实际应用效果.结果表明,深度学习技术能够显著提升PPE检测的准确性与效率,尤其在复杂场景和小目标检测中表现突出.然而,当前检测技术在数据集多样性、设备稳定性和小目标检测等方面仍存在挑战.未来需要进一步优化多模态检测技术,并结合5G与边缘计算等新兴技术,以实现更高效、精准的PPE检测与应用.
Abstract
This study aims to analyze and summarize the development of personal protective equipment(PPE)detec-tion technology and its applications in construction site safety management,focusing on the role and effectiveness of sen-sors and deep learning in detection.A combination of literature review and data analysis was employed,with the Citespace system used to map recent advancements in PPE detection.By comparing sensor-based detection methods with deep learn-ing algorithms,including One-stage and Two-stage models,this paper comprehensively evaluates their practical effective-ness in PPE detection.The results indicate that deep learning techniques significantly enhance the accuracy and efficiency of PPE detection,particularly in complex environments and small-object detection.However,current detection technolo-gies still face challenges in terms of dataset diversity,equipment stability,and small-object detection.The conclusion high-lights the need for further optimization of multimodal detection technologies,along with the integration of emerging tech-nologies such as 5G and edge computing,to achieve more efficient and precise PPE detection and application.
关键词
个人防护装备/检测/计算机视觉/深度学习/传感器Key words
personal protective equipment/detection/computer vision/deep learning/sensor引用本文复制引用
出版年
2024
NETL
NSTL
万方数据