煤矿安全2025，Vol.56Issue(6)：79-88,10.DOI:10.13347/j.cnki.mkaq.20240347
基于改进型YOLOv5的粉尘检测算法
Dust detection algorithm based on improved YOLOv5
陈清华 1张俊伟 2程迎松 3张旭 4程建华5
作者信息
- 1. 安徽理工大学 机电工程学院,安徽 淮南 232001||安徽理工大学 环境友好材料与职业健康研究院(芜湖),安徽 芜湖 241003||安徽理工大学 矿山智能装备与技术安徽省重点实验室,安徽 淮南 232001
- 2. 安徽理工大学 机电工程学院,安徽 淮南 232001||安徽理工大学 矿山智能装备与技术安徽省重点实验室,安徽 淮南 232001
- 3. 安徽理工大学 机电工程学院,安徽 淮南 232001||安徽理工大学 矿山智能装备与技术安徽省重点实验室,安徽 淮南 232001||安徽理工大学 机械工业矿山装备智能化实验室,安徽 淮南 232001
- 4. 安徽理工大学 矿山智能装备与技术安徽省重点实验室,安徽 淮南 232001
- 5. 安徽理工大学 机电工程学院,安徽 淮南 232001
- 折叠
摘要
Abstract
In recent years,dust detection methods based on image recognition have received full attention and development because they do not have installation and detection range limitations,but the real-time and accuracy of existing methods still need to be im-proved.In view of this,we propose a dust image detection method based on the improved YOLOv5 algorithm.Firstly,the existing YOLOv5 algorithm backbone network and Neck network were improved,and the original backbone network was replaced by Ghost-Net,a lightweight network,to reduce network parameters,and then three feature layers were output.Then,for the three feature layers of the backbone network output,the attention mechanism CA is applied to increase the network accuracy.Finally,ablation experiments and comparative experiments were designed to verify the effectiveness of the improved algorithm.The experimental results show th-at the mean Average Precision(mAP)of the improved algorithm can reach 92.11%and the detection speed reaches 37 frames persecond.关键词
粉尘图像检测/改进YOLOv5 算法/置信度/轻量化网络/CA注意力机制Key words
dust image detection/improved YOLOv5 algorithm/confidence coefficient/Lightweight network/CA attention mech-anism分类
矿业与冶金引用本文复制引用
陈清华,张俊伟,程迎松,张旭,程建华..基于改进型YOLOv5的粉尘检测算法[J].煤矿安全,2025,56(6):79-88,10.
