[1]张　震,肖宗荣,李友好,等.基于改进YOLOv7的高风险区工程车辆识别算法[J].郑州大学学报(工学版),2025,46(05):1-8.[doi:10.13705/j.issn.1671-6833.2025.02.019]
　ZHANG Zhen,XIAO Zongrong,LI Youhao,et al.Construction Vehicles Recognition Algorithm Based on Improved YOLOv7 in High Risk Areas[J].Journal of Zhengzhou University (Engineering Science),2025,46(05):1-8.[doi:10.13705/j.issn.1671-6833.2025.02.019]
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基于改进YOLOv7的高风险区工程车辆识别算法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
46
期数:
2025年05期
页码:
1-8
栏目:
出版日期:
2025-08-10

文章信息/Info

Title:
Construction Vehicles Recognition Algorithm Based on Improved YOLOv7 in High Risk Areas
文章编号:
1671-6833(2025)05-0001-08
作者:
张　震1;  肖宗荣2;  李友好3;  黄伟涛3
1.郑州大学 电气与信息工程学院,河南 郑州 450001;2.郑州大学 计算机与人工智能学院,河南 郑州 450001; 3.河南汇融油气技术有限公司,河南 郑州 450001
Author(s):
ZHANG Zhen1;  XIAO Zongrong2;  LI Youhao3;  HUANG Weitao3
1.School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China; 2.School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China; 3.Henan Huirong Oil and Gas Technology Co., Ltd., Zhengzhou 450001, China
关键词:
高风险区;  工程车辆;  YOLOv7;  注意力机制;  上采样器;  特征提取
Keywords:
highrisk areas;  construction vehicles;  YOLOv7;  attention mechanism;  upsampling;  feature extraction
分类号:
TP391
DOI:
10.13705/j.issn.1671-6833.2025.02.019
文献标志码:
A
摘要:
为解决高风险区域工程车辆施工时对天然气管道的安全威胁问题,特别是重型车辆可能带来的物理冲击与环境干扰,提出一种基于改进YOLOv7的工程车辆识别算法。以6种施工现场常见的自卸车、压路车、搅拌车、叉车、挖掘机和装载车等车型为研究对象,利用自定义数据集进行训练,数据集涵盖多种环境和角度的图像,确保模型效能。首先,在YOLOv7头部网络中引入了CBAM注意力机制并在最大池化层结构中增加了改进的GAM注意力机制,提升模型对关键图像特征的关注度,从而提高目标检测的准确性;其次,采用DySample动态上采样器替换最近邻插值上采样模块,提高检测精度;最后,提出了一种改进的SPPCSPC模块,提高特征提取效率,降低计算成本,加速推理过程。这些改进使得模型在图像质量低、目标距离远等挑战下仍能维持高检测精度。实验结果表明:所提算法在自定义工程车数据集上的精确度P、召回率R、mAP@0.5、mAP@0.5∶0.95分别为97.7%、94.7%、98.6%、90.4%;与YOLOv7算法相比,P、R、mAP@0.5、mAP@0.5∶0.95分别提升了1.3百分点、1.4百分点、1.4百分点、3.7百分点。
Abstract:
To address the safety risks posed by construction vehicles operations in highrisk areas near natural gas pipelines, particularly the physical impacts and environmental disturbances caused by heavy vehicles, in this study an improved YOLOv7-based construction vehicles recognition algorithm was proposed. Six common types of construction vehicles including dump trucks, rollers, mixers, forklifts, excavators, and loaders were selected as the research objects. A custom dataset, containing images captured in various environments and angles, was used to train the model, ensuring its performance. Firstly, the CBAM attention mechanism was introduced into the YOLOv7 head, and an improved GAM attention mechanism was added to the max pooling layer to enhance the model′s focus on key image features and improve detection accuracy. Secondly, the DySample dynamic upsampling module replaced the nearest neighbor interpolation, boosting precision. Finally, an improved SPPCSPC module was designed to enhance feature extraction efficiency, reduce computational costs, and accelerate inference. These modifications could enable the model to maintain high detection accuracy even in challenging scenarios such as low-quality images or distant targets. Experimental results demonstrated that the proposed algorithm achieved a precision P of 97.7%, recall R of 94.7%, mAP@0.5 of 98.6%, and mAP@0.5∶0.95 of 90.4%. Compared to the original YOLOv7 algorithm, these metrics improved by 1.3, 1.4, 1.4, and 3.7 percentage points, respectively.

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更新日期/Last Update: 2025-09-19