摘要
在煤矿生产中,工人由于未佩戴安全帽而受伤的事故时有发生。为了构建数字化安全帽监测系统,提出了一种基于卷积神经网络的安全帽佩戴检测模型。采用先进的Darknet53网络作为模型主干,用于提取图片的特征信息。此外,在模型中引入注意力机制用于丰富特征之间的信息传播,增强模型的泛化能力。最后,制作了安全帽佩戴预训练数据集和实际矿井场景数据集,并在PyTorch平台进行全面的对比实验验证了模型设计的有效性,模型在实际矿井场景数据集上获得92.5 mAP的优异性能。
In the production of coal mines,accidents happen to workers once in a while because of absence of safety helmet.In order to establish digital safety helmet detection system,a wearing safety helmet detection model based on convolutional neural networks is proposed.Specifically,the model is based on advanced Darknet53 as model backbone,which is used to extract feature information from pictures.In addition,attention mechanism is introduced to enrich the propagation of information between features,enhancing the generalization of model.Finally,a wearing safety helmet pre-training dataset and a real mine scene dataset are built,and comprehensively comparative experiments are conducted on PyTorch platform to verify the effectiveness of the model designs,which achieves an excellent performance of 92.5 mAP on the real mine scene dataset.
作者
刘欣
张灿明
Liu Xin;Zhang Canming(Anhui Academy of Coal Science,Hefei 230001,China)
出处
《电子技术应用》
2020年第9期38-42,46,共6页
Application of Electronic Technique
基金
安徽科技创新战略与软科学研究专项项目(1706a02020044)
安徽省重点研究与开发计划项目(1704a0902063)。
关键词
安全帽佩戴检测
深度学习
卷积神经网络
注意力机制
wearing safety helmet detection
deep learning
convolutional neural networks
attention mechanism
