郑州大学学报（工学版）2025，Vol.46Issue(5)：1-8,8.DOI:10.13705/j.issn.1671-6833.2025.02.019
基于改进YOLOv7的高风险区工程车辆识别算法
Construction Vehicles Recognition Algorithm Based on Improved YOLOv7 in High Risk Areas
摘要
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 con-struction vehicles including dump trucks,rollers,mixers,forklifts,excavators,and loaders were selected as the re-search 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 re-placed 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 al-gorithm,these metrics improved by 1.3,1.4,1.4,and 3.7 percentage points,respectively.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Key words
highrisk areas/construction vehicles/YOLOv7/attention mechanism/upsampling/feature extraction分类
信息技术与安全科学引用本文复制引用
张震,肖宗荣,李友好,黄伟涛..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(5):1-8,8.基金项目
河南省重点研发专项项目(231111211600) （231111211600）
河南省重大公益专项项目(201300311200) （201300311200）
