郑州大学学报（工学版）2025，Vol.46Issue(3)：19-25,104,8.DOI:10.13705/j.issn.1671-6833.2025.03.002
基于改进YOLOv8n的施工场景下防护装备佩戴检测算法
Protective Equipment Wearing Detection Algorithm in Construction Scenarios Based on YOLOv8n
摘要
Abstract
In view of the problems of protective equipment detection,such as information interference,uneven illu-mination and occlusion in the construction scene,a lightweight algorithm with improved YOLOv8n was proposed,which was called YOLO-LA.Firstly,the weighted bidirectional feature pyramid network BiFPN was introduced in-to the neck,and the underlying details and high-level semantic information were improved through multi-path inter-active fusion,the multi-scale feature fusion performance was enhanced,and the detection accuracy of the model for small targets in complex scenes was improved.Secondly,the C2f-ContextGuided module was used to transform the backbone network in the baseline model,and the global context information was used to calculate the weight vector,to refine the joint features of the local features and the surrounding context features,so as to improve the feature ex-traction ability of the model and reduce the complexity of the model.Then,a new LSCD lightweight detection head was proposed,which used shared convolution to reduce the number of parameters and computations of the model.Finally,EIoU was used to replace the original CIoU,and the border regression was optimized,and the convergence speed and regression accuracy of the algorithm were improved.Compared with the baseline model YOLOv8n,the number of parameters,the amount of computation,and the size of the model were reduced by 61.5%,43.2%and 58.7%,respectively,and the mAP@0.5 was increased by 1.4 percentage points,and the FPS was 253 frames/s,which could meet the requirements of real-time,accuracy and lightweight of protective equipment wearing detec-tion.关键词
防护装备检测/BiFPN/LSCD/EIoU损失/C2f-ContextGuided模块/模型轻量化Key words
protective equipment detection/BiFPN/LSCD/EIoU loss/C2f-ContextGuided module/model lightweighting分类
计算机与自动化引用本文复制引用
李军,周科宇,邹军,曾文炳..基于改进YOLOv8n的施工场景下防护装备佩戴检测算法[J].郑州大学学报（工学版）,2025,46(3):19-25,104,8.基金项目
国家自然科学基金资助项目(51305472) （51305472）
重庆市研究生培养项目(JDLHPYJD2018003) （JDLHPYJD2018003）
