安全与环境工程2024，Vol.31Issue(5)：18-27,10.DOI:10.13578/j.cnki.issn.1671-1556.20230589
基于深度学习与深度估计的施工机械危险区域侵入智能预警方法
Intelligent early warning method for construction machinery hazardous area intrusion based on deep learning and depth estimation
摘要
Abstract
To solve the problem of engineering safety accidents caused by workers and construction machinery intruding into construction hazardous areas,etc.,a multi-task-driven dynamic identification and early warning method for hazardous area intrusion events is proposed.A Yolov8 network with permutation variable convolu-tional DConv2 module was used for target class detection and coordinate outer contour extraction to improve the recognition accuracy of mobile construction machinery.It was also combined with the Monodepth2 monocular depth estimation network for depth information estimation and coordinate unification to calculate the true state of a worker or construction machine at a distance from a hazardous area event,which is used to assess the risk of hazardous area intrusion.The model performance was compared with Yolov8,the original Yolov8 and Yolov5 models with different modification layers and four scenarios were designed for model performance validation.The study shows that the model improves 2.99%and 3.55%in construction machinery identification accuracy and contour extraction accuracy respectively,and can maintain an accuracy rate of over 94%in the identification of workers and construction machinery intrusion into mobile construction machinery hazardous area,with an FPS of around 17.7,which can effectively achieve intelligent dynamic warning of construction hazardous area intrusion.关键词
施工安全/危险区域/侵入事件预警/人机碰撞/深度学习/深度估计Key words
construction safety/hazardous area/intrusion event early warning/human-machine collision/deep learning/depth estimation分类
资源环境引用本文复制引用
吴晗,韩豫..基于深度学习与深度估计的施工机械危险区域侵入智能预警方法[J].安全与环境工程,2024,31(5):18-27,10.基金项目
国家自然科学基金面上项目(72071097) （72071097）
教育部人文社会科学研究规划基金项目(20YJAZH034) （20YJAZH034）
