基于改进YOLOv8的大气污染烟雾检测方法研究
Research on Air Pollution Smoke Detection Method Based on Improved YOLOv8
吴桂玲 1张耀军 1葛伟 2韩敏2
作者信息
- 1. 信阳农林学院信息工程学院,河南信阳 464000
- 2. 河南省信阳生态环境监测中心,河南信阳 464000
- 折叠
摘要
大气污染烟雾检测在环境监测领域扮演着至关重要的角色,能够准确检测出烟雾排放源.本文提出了一种大气污染烟雾检测的模型YOLOv8n-SC.首先,采用Slim-Neck网络对YOLOv8n的颈部网络进行改进,可以极大减少冗余,降低模型复杂度,提高检测速度.其次,对原始模型中的上采样算子进行改进,用CARAFE取代最近算子,获得更精确的采样结果和更精细的定位信息.最后,建立烟雾数据集,并采用Copy-Pasting方法对建立的烟雾数据集进行增强,可以生成具有微小变化的新样本,从而扩展训练数据集,以提升模型的性能.研究结果表明,YOLOv8n-SC模型相比原始的YOLOv8n模型,参数量降低了 6.38％,平均均值精度提升了 2.7％.该模型不仅模型较小易于部署,且还保证了检测精度要求.
Abstract
The detection of atmospheric pollution smoke plays a crucial role in the field of environmental monitoring,enabling the accurate identification of smoke emission sources.This paper proposes a novel atmospheric pollution smoke detection mod-el,YOLOv8n-SC.Firstly,the Slim-Neck network is employed to enhance the neck section of YOLOv8n,significantly re-ducing redundancy,simplifying the model complexity,and accelerating the detection speed.Secondly,the original model's up-sampling operator is improved by replacing the nearest neighbor operator with CARAFE,resulting in more precise sampling results and finer localization information.Finally,a smoke dataset is established,and the Copy-Pasting method is used to augment the dataset,generating new samples with subtle variations to expand the training dataset and enhance model perform-ance.The research findings indicate that compared to the original YOLOv8n model,the YOLOv8n-SC model achieves a 6.38％reduction in the number of parameters and a 2.7％improvement in mean average precision.This model is not only com-pact and easy to deploy but also meets the requirements for detection accuracy.
关键词
大气污染/烟雾检测/YOLOv8模型/YOLOv8n-SC模型Key words
air pollution/smoke detection/YOLOv8 model/YOLOv8n-SC model引用本文复制引用
基金项目
河南省高等学校重点科研项目(24B520035)
出版年
2024
NETL
NSTL
万方数据