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基于YOLOv5改进模型的办公室吸烟行为检测

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为解决目前吸烟行为检测中小目标检测精度低、易误检的问题,提出一种改进YOLOv5的识别模型YO-LOv5s+.该模型将YOLOv5的主干网络与BoTNet相结合,以提高模型的特征提取能力,使其能够检测更小的目标物体;同时改进特征融合部分,在网络模型的颈部应用加权双向特征金字塔BiFPN,以高效融合浅层位置信息与深层高级语义信息,有效提高了检测精度.将网络公开数据集与自制数据集整合成办公室吸烟实验数据集,在该数据集上比较YOLOv5s+模型与原YOLOv5模型的检测性能.实验结果表明,改进模型YOLOv5s+的平均精度均值(mAP)为81.8%,精度为82.8%,召回率为83.9%,相较原模型分别提高了5.4%、4.1%和6.4%,较好地实现了办公室吸烟行为检测.
Office Smoking Behavior Detection Based on Improved YOLOv5 Model
To solve the problems of low accuracy and easy false detection of small targets in current smoking behavior detection,an improved YOLOv5 recognition model YOLOv5s+is proposed.This model combines the backbone network of YOLOv5 with BoTNet to improve the feature extraction ability of the model,enabling it to detect smaller target objects;At the same time,the feature fusion part is improved by applying a weighted bidirectional feature pyramid BiFPN in the neck of the network model to efficiently fuse shallow position information and deep high-level semantic information,effectively improving detection accuracy.Integrate publicly available online datasets and self-made datasets into an office smoking experimental dataset,and compare the detection performance of the YOLOv5s+model with the original YOLOv5 model on this dataset.The experimental results show that the average accuracy(mAP)of the improved model YOLOv5s+is 81.8%,with an accuracy of 82.8%and a recall rate of 83.9%.Compared with the original model,it has improved by 5.4%,4.1%,and 6.4%,respectively,and has achieved good detection of office smoking behavior.

deep learningYOLOv5smoking detectionfeature fusion

魏袁慧、方睿、石兴、刘金智

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成都信息工程大学 计算机学院,四川 成都 610225

深度学习 YOLOv5 吸烟检测 特征融合

2024

软件导刊
湖北省信息学会

软件导刊

影响因子:0.524
ISSN:1672-7800
年,卷(期):2024.23(9)