煤矿安全2025，Vol.56Issue(8)：48-58,11.DOI:10.13347/j.cnki.mkaq.20240469
基于机器视觉的粉尘识别与浓度检测方法研究
Research on dust identification and concentration detection method based on machine vision
摘要
Abstract
Aiming at the problem that the current machine vision algorithm fails to combine position information with concentration value in the field of dust detection,we propose an algorithm that combines improved YOLOv5 with multivariate model.Firstly,a set of simulation experiment platform for collecting and making dust data set is built,and the dust position information data set and dust concentration data set are made respectively.Then,the improved YOLOv5 is used to train the dust position information data set to obtain the training weight.At the same time,the dust image in the dust concentration data set is transformed into different color spaces,and the color features and texture features are extracted.The relationship between these features and dust concentration is analyzed to establish a multivariate model.Finally,the multivariate model is combined with YOLOv5 to obtain the ability to identi-fy dust location information and detect its concentration in real time.The experimental results show that the improved YOLOv5 dust recognition model improves the accuracy and recall rate by 2.6%and 3.1%respectively compared with the original model.The ac-curacy rate reaches 91.8%and the recall rate reaches 90.8%.After combining with the multivariate model,the algorithm obtains the dust concentration detection ability,the detection accuracy reaches 98.79%,and the adjustment accuracy reaches 96.03%.关键词
机器视觉/粉尘识别/粉尘浓度检测/改进YOLOv5/多变量模型Key words
machine vision/dust identification/dust concentration detection/improved YOLOv5/multi-variable modeling分类
资源环境引用本文复制引用
屠陆阳,陈清华,程迎松,江丙友..基于机器视觉的粉尘识别与浓度检测方法研究[J].煤矿安全,2025,56(8):48-58,11.基金项目
国家重点研发计划资助项目(2022YFC2503201) （2022YFC2503201）
安徽理工大学环境友好材料与职业健康研究院研发专项基金资助项目(ALW2021YF12) （ALW2021YF12）
安徽省省级质量工程资助项目(13230325) （13230325）
