计算机工程与应用2026，Vol.62Issue(2)：103-115,13.DOI:10.3778/j.issn.1002-8331.2505-0114
基于ME-YOLO11的工人不安全行为图像检测算法
ME-YOLO11-Based Image Detection Algorithm for Unsafe Worker Behaviors
摘要
Abstract
In industrial production and construction sites,unsafe worker behaviors such as not wearing safety helmets,smoking,and using mobile phones are major causes of safety accidents,and thus require accurate recognition and real-time monitoring.However,due to the small size of targets like helmets,cigarettes,and phones,the existing YOLO 11 ob-ject detection algorithm suffers from missed and false detections.The core reasons lie in the limited multi-scale feature representation capability of its backbone and information loss during cross-layer feature fusion in the neck.To address these issues,this paper proposes an improved algorithm,ME-YOLO11.A new feature processing module,C3k2_MLCA,is introduced to replace the original C3k2 in the backbone,enhancing the model's ability to extract features from small objects.Additionally,an enhanced feature fusion pyramid network(EFFPN)is designed in the neck,incorporating DySample dynamic upsampling,a content-guided attention fusion layer,and a micro-target fusion layer to strengthen multi-scale fea-ture interaction.Experimental results show that ME-YOLO11 achieves 91.2％mAP@0.5 and 64.0％mAP@0.5:0.95 on the self-built WUBD dataset,and 38.5％and 23.2％respectively on the VisDrone2019 dataset,demonstrating excellent performance in small object detection and strong generalization capability.关键词
安全管理/目标检测/YOLO11/DySample/特征融合Key words
safety management/object detection/YOLO11/DySample/feature fusion分类
信息技术与安全科学引用本文复制引用
吴彬,王朝立,孙占全..基于ME-YOLO11的工人不安全行为图像检测算法[J].计算机工程与应用,2026,62(2):103-115,13.基金项目
国家自然科学基金(62173232). （62173232）
