摘要
人的不安全行为是导致煤矿事故发生的主要原因之一,随着以深度学习为代表的人工智能技术的成熟,YOLOv5s目标检测算法也可以应用于煤矿井下不安全行为的智能识别中。但是由于煤矿井下环境复杂等原因,导致YOLOv5s模型检测效果受到影响。针对该问题,对YOLOv5s模型进行改进,通过添加小目标检测层来提高算法对小目标(如安全帽)的检测性能;之后使用DIoU损失函数替代CIoU以提高目标检测模型的精度;最后在CUMT-HelmeT数据集进行训练,训练结果显示,改进后的算法mAP@0.5达到了90.4%,在原始88.6%的基础上提高了1.8%。改进后的检测算法为矿工不安全行为智能识别奠定了理论基础。
Human unsafe behavior is one of the main causes of coal mine accidents.With the maturity of artificial intelligence technology represented by deep learning,the YOLOv5s object detection algorithm can also be applied to the intelligent recognition of unsafe behaviors in coal mines,but due to the complex underground environment of coal mines,the detection effect of YOLOv5s model is affected.We improve the YOLOv5s model,and boost the detection of small targets(such as hard hats)by adding a small target detection layer.Then,the DIoU loss function was used to replace CIoU to improve the accuracy of the object detection model.Finally,the CUMT-HelmeT dataset is trained,and the training results show that the mAP@0.5 of the improved algorithm reaches 90.4%,which is 1.8%higher than the original 88.6%.The improved detection algorithm lays a theoretical foundation for the intelligent identification of miners’unsafe behaviors.
作者
王冲
姚有利
侯艳文
刘怡汝
WANG Chong;YAO Youli;HOU Yanwen;LIU Yiru(School of Coal Engineering,Shanxi Datong University,Datong 037003,China)
出处
《陕西煤炭》
2025年第7期180-184,共5页
Shaanxi Coal
基金
大同科技项目(2023067)
山西大同大学研究生实践创新类项目(2024SJCX14)。
