[1]张　震,张晨稳,张俊杰,等.改进YOLOv7-tiny的施工现场安全衣帽穿戴检测算法[J].郑州大学学报(工学版),2026,47(02):1-8.[doi:10.13705/j.issn.1671-6833.2025.05.001]
　ZHANG Zhen,ZHANG Chenwen,ZHANG Junjie,et al.Improved YOLOv7-tiny for Safety Clothing and Helmet Wearing Detection Algorithm on Construction Site[J].Journal of Zhengzhou University (Engineering Science),2026,47(02):1-8.[doi:10.13705/j.issn.1671-6833.2025.05.001]
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改进YOLOv7-tiny的施工现场安全衣帽穿戴检测算法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
47
期数:
2026年02期
页码:
1-8
栏目:
出版日期:
2026-02-13

文章信息/Info

Title:
Improved YOLOv7-tiny for Safety Clothing and Helmet Wearing Detection Algorithm on Construction Site
文章编号:
1671-6833(2026)02-0001-08
作者:
张　震1;  张晨稳2;  张俊杰3;  裴胜利3;  王文娟4
1.郑州大学 电气与信息工程学院,河南 郑州 450001;2.郑州大学 河南先进技术研究院,河南 郑州 450001;3.河南汇融油气技术有限公司,河南 郑州 450001;4.广东省轻工业技师学院 机电工程学院,广东 广州 511330
Author(s):
ZHANG Zhen1;  ZHANG Chenwen2;  ZHANG Junjie3;  PEI Shengli3;  WANG Wenjuan4
1.School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China; 2.School of Henan Institute of Advanced Technology, Zhengzhou University, Zhengzhou 450001, China; 3.Henan Huirong Oil & Gas Technology Co., Ltd., Zhengzhou 450001, China; 4.School of Mechanical and Electrical Engineering, Guangdong Province Technician College of Light Industry, Guangzhou 511330, China
关键词:
YOLOv7-tiny;  注意力机制;  RFEM;  Shape-IoU;  安全衣帽检测
Keywords:
YOLOv7-tiny;  attention mechanism;  RFEM;  Shape-IoU;  safety clothing and helmet delection
分类号:
TP391
DOI:
10.13705/j.issn.1671-6833.2025.05.001
文献标志码:
A
摘要:
针对当前施工现场安全衣帽穿戴检测算法在复杂背景、弱光环境及目标遮挡情况下的抗干扰能力不足,导致检测精度低、漏检率高及误检现象频繁等问题,提出了一种改进YOLOv7-tiny的施工现场安全衣帽穿戴检测算法。首先,在特征提取区域引入EMA注意力机制增强网络特征提取能力,弱化复杂背景干扰;其次,在特征融合部分插入RFEM模块提升网络感受野,获取更广泛的上下文信息,增强对小目标的感知能力;最后,采用Shape-IoU替换IoU边界回归损失函数,提升检测准确性。实验结果表明:改进模型在自制数据集上的mAP@0.5达到90.4%,相比原模型提高3.0百分点;帧率达到了93帧/s,模型参数量仅为6.1×106。相比YOLOv8s、YOLOv9s等模型,所提算法在检测精度、速度和模型轻量化方面更具优势,适合施工现场的实时检测应用。
Abstract:
In response to the insufficient robustness of current safety clothing and helmet wearing detection algorithm in complex backgrounds, and frequent false positive rates, an improved YOLOv7-tiny detection algorithm for safety clothing and helmet wearing on construction site was proposed. Firstly, the EMA attention mechanism was introduced into the feature extraction module to enhance the networks feature extraction capability and mitigate complex background interference. Secondly, the RFEM module was integrated into the feature fusion stage to improve the networks receptive field, acquiring broader contextual information, and enhance perception for small targets. Finally, Shape-IoU was employed to replace the IoU boundary regression loss function, to improve detection accuracy. Experimental results showed that the improved model achieved a mAP@0.5 of 90.4% on the proprietary dataset, 3.0 percentage points higher than the original model. The frame rate speed reached 93 frames/s, and the model size comprised only 6.1 million parameters. Compared to YOLOv8s, YOLOv9s, and other models, the proposed algorithm demonstrated superior performance in detection accuracy, speed, and model efficiency, making it suitable for real-time detection applications on construction site.

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更新日期/Last Update: 2026-03-04