混叠场景下的轻量级口罩佩戴检测算法
Lightweight mask wearing detection algorithm in aliasing scenario
安祯阳1
作者信息
- 1. 重庆交通大学机电与车辆工程学院,重庆 400074
- 折叠
摘要
针对混叠场景下口罩佩戴检测识别率低,而现有检测模型结构复杂难以部署的难题,提出了一种轻量级口罩佩戴检测算法.首先,轻量化网络MobileNetv3作为混叠场景图像的特征提取网络;其次提出通道混洗,空间上利用不同感受野的卷积核进行特征提取的注意力机制,实现特征信息的强化;最后设计了损失函数解决了数据类不平衡问题,提高了模型检测精度.在公开数据集测试表明,模型平均检测精度为78.1%,FPS达到65.53 Hz,满足在小型设备部署的要求.
Abstract
A lightweight mask wearing detection algorithm is proposed to address the low recognition rate of mask wearing de-tection in mixed scenes and the difficulty of deploying existing detection models with complex structures.Firstly,the lightweight network MobileNetv3 serves as a feature extraction network for aliasing scene images;Secondly,channel mixing is proposed,which utilizes convolutional kernels from different receptive fields in space for feature extraction and enhances feature information;Finally,a loss function was designed to solve the problem of imbalanced data classes and improve the accuracy of model detection.In public dataset testing,the average detection accuracy of the model is 78.1%,and the FPS reaches 65.53 Hz,meeting the require-ments for deployment on small devices.
关键词
轻量级/目标检测/部分卷积/特征融合Key words
lightweight/target detection/partial convolution/feature fusion引用本文复制引用
出版年
2024
NETL
NSTL
维普
万方数据