摘要
煤矿工人在井下开展生产作业时,头戴安全帽是有效保障其人身安全的重要防护措施,近年来由于未佩戴安全帽造成的事故层出不穷。针对这个问题,设计了安全帽佩戴检测算法。首先使用煤矿井下摄像头拍摄图像、公开数据集和网络爬虫的方式构建数据集,其次使用YOLOv5对构建的数据集进行训练,训练出安全帽检测模型,最后把训练的模型部署到监控终端进行测试。测试结果表明,设计的算法能够正确识别作业人员是否佩戴安全帽。
When coal miners carry out production operations underground,wearing helmets is an important protective measure to effectively guarantee their personal safety.To address this problem,a helmet wearing detection algorithm is designed.Firstly,the dataset is constructed using images taken by underground coal mine cameras,public datasets and web crawlers,secondly,the constructed dataset is trained using YOLOv5 to train the helmet detection model,and finally the trained model is deployed to the monitoring terminal for testing.The test results show that the designed algorithm is able to correctly identify whether the operator is wearing a helmet or not.
作者
肖淑艳
吴宗绍
XIAO Shuyan;WU Zongshao(School of Electrical&Information Engineering,Jiangsu University of Technology,Changzhou Jiangsu 221000,China)
出处
《信息与电脑》
2022年第17期177-179,共3页
Information & Computer
