摘要
在工业生产中,安全帽对人体头部提供了较好的安全保障。在现场环境中,检验施工人员是否佩戴安全帽主要依靠人工检查,因而效率非常低。为了解决施工现场安全帽检测识别难题,提出一种基于深度级联网络模型的安全帽检测方法。首先通过You Only Look Once version 4(YOLOv4)检测网络对施工人员进行检测;然后运用注意力机制残差分类网络对人员ROI区域进行分类判断,识别其是否佩戴安全帽。该方法在Ubuntu18.04系统和Pytorch深度学习框架的实验环境中进行,在自主制作工业场景安全帽数据集中进行训练和测试实验。实验结果表明,基于深度级联网络的安全帽识别模型与YOLOv4算法相比,准确率提高了2个百分点,有效提升施工人员安全帽检测效果。
In industrial production,safety helmet provides a better safety guarantee for human head.In the field environment,the inspection of whether the construction personnel wear safety helmet mainly depends on manual inspection,so the efficiency is very low.In order to solve the problem of helmet detection and identification in construction site,this paper proposes a helmet detection method based on the deep cascade network model.Firstly,the construction personnel are detected through the You Only Look Once version 4(YOLOv4)detection network.Then,the attention mechanism residual classification network is used to classify and judge the ROI region of personnel and identify whether they wear a helmet or not.This method is carried out in the experimental environment of Ubuntu18.04 system and Pytorch deep learning framework,and training and testing experiments are carried out in the self-produced helmet data set.The experimental results show that compared with YOLOv4,the safety helmet recognition model based on the deep cascade network has an accuracy increase of 2 percentage points,which effectively improves the safety helmet detection effect of construction personnel.
作者
杨贞
朱强强
彭小宝
殷志坚
温海桥
黄春华
YANG Zhen;ZHU Qiang-qiang;PENG Xiao-bao;YIN Zhi-jian;WEN Hai-qiao;HUANG Chun-hua(College of Communication&Electronics,Jiangxi Science and Technology Normal University,Nanchang 330013,China;Fangchenggang City Meteorological Bureau,Fangchenggang 538001,China)
出处
《计算机与现代化》
2022年第1期91-97,119,共8页
Computer and Modernization
基金
国家自然科学基金资助项目(61866016)
江西省自然科学基金面上项目(20202BABL202014)
江西省教育厅一般项目(GJJ190587)
江西省教育厅青年项目(GJJ201142)
江西科技师范大学青年拔尖项目(2018QNBJRC002)。
关键词
安全帽
级联网络
目标检测
YOLOv4
残差分类网络
注意力机制
safety helmet
cascade network
target detection
You Only Look Once version 4(YOLOv4)
residual classification network
attention mechanism
