基于深度学习的安全帽监管系统

Safety Helmet Supervision System Based on Deep Learning

在工程现场因不佩戴安全帽导致的悲剧时有发生,为了协助工程现场管理人员保障工人的人身安全,本文设计实现了一种基于深度学习的安全帽智能监管系统.该系统采用集速度和精度为一体的YOLOv4目标检测模型,在数据集上使用K-means算法聚类分析生成新的先验框,并使用新的先验框进行训练,将安全帽的检测精度提高至92%;将检测模型YOLOv4与跟踪模型DeepSORT相结合,有效解决重复警告和无法对违规数据进行统计的问题;最终制作成跨平台移动APP,方便管理人员使用移动端随时随地监管安全帽佩戴情况.本系统是一套集安全帽检测、检测视频实时直播、智能警告、违规图片抓取并展示,违规数据可视化等功能为一体的安全帽智能监管系统,它的使用能够大大提高工程现场的生产安全系数和监管效率.

Tragedies caused by not wearing safety helmets occur from time to time in engineering sites. To assist site managers in protecting workers’ safety, this study has designed and implemented an intelligent safety helmet supervision system based on deep learning. The system adopts the YOLOv4 target detection model integrating speed and accuracy,generates the new anchor boxes by K-means clustering analysis on the data set, and then trains the model with the new anchor boxes. The detection accuracy of the helmet is improved to 92%. The detection model YOLOv4 is combined with the tracking model DeepSORT to effectively solve the problems of repeated warnings and failures to produce statistics on illegal data. Finally, it is built into a cross-platform mobile APP, which is convenient for managers to use the mobile terminal to monitor the helmet-wearing situation anytime and anywhere. This intelligent supervision system covers a set of functions including safety helmet detection, real-time broadcast of detection videos, intelligent warning, illegal picture capture and display, and illegal data visualization. It can greatly improve the production safety factors and supervision efficiency in the project site.