计算机工程与应用2024，Vol.60Issue(17)：203-215,13.DOI:10.3778/j.issn.1002-8331.2402-0226
FLM-YOLOv8:一种轻量级的口罩佩戴检测算法
FLM-YOLOv8:Lightweight Mask Wearing Detection Algorithm
摘要
Abstract
Aiming at the problems that the existing mask wearing detection model can't balance the detection accuracy and speed well,the parameters are large,and the rate of missed and false detection is high,a lightweight mask wearing detec-tion algorithm FLM-YOLOv8 is proposed.Firstly,the lightweight FasterNet is used to replace the backbone feature extraction network of YOLOv8n to improve the network detection speed.Secondly,the C2f module is improved by com-bining FasterNet Block to reduce the computational complexity of the model.Then,the structure of SPPF-LSKA is pro-posed to enhance the feature expression ability and perception ability of the model and improve the network detection accuracy.Finally,the Inner-MPDIoU bounding box regression loss function is designed to improve the regression prediction accuracy and accelerate the convergence speed.In addition,a mask wearing data set marked with a complex and diverse scene is created and enhanced with mosaic data to improve the network generalization ability.The experimental results show that the mAP@0.5 of the algorithm on the targets wearing masks correctly,not wearing masks correctly and not wearing masks reaches 91.3%,and the FPS reaches 143.6,which realizes more real-time and accurate mask wearing detection.关键词
口罩佩戴检测/YOLOv8/FasterNet/轻量级/损失函数Key words
mask wearing detection/YOLOv8/FasterNet/lightweight/loss function分类
信息技术与安全科学引用本文复制引用
高民,陈高华,古佳欣,张春美..FLM-YOLOv8:一种轻量级的口罩佩戴检测算法[J].计算机工程与应用,2024,60(17):203-215,13.基金项目
山西省自然科学基金(202203021211198) （202203021211198）
太原科技大学博士启动基金(20222026). （20222026）
