计算机技术与发展2025，Vol.35Issue(3)：34-39,6.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0329
基于YOLOv8n的施工场景下安全帽佩戴检测算法
Detection Algorithm for Helmet Wearing in Construction Scenarios Based on YOLOv8n
摘要
Abstract
To address the issues of a significant number of small-sized targets,susceptibility to occlusion,and environmental interference in the task of safety helmet detection in construction scenarios,an improved YOLOv8n-based safety helmet detection algorithm is proposed.Firstly,a bidirectional feature pyramid network(BiFPN)is designed to enhance feature expression through skip connections,optimize feature fusion,and add a 160×160 effective feature layer to further improve the detection accuracy of small targets.Secondly,the detector module is restructured using grouped convolution and parameter-sharing strategies to reduce model parameters while maintaining detection accuracy.Lastly,an attention guidance module combining large separable kernel attention(LSKA)and bottleneck attention module(BAM)is introduced and integrated into the spatial pyramid pooling(SPPF)module.LSKA expands the receptive field,improving the model's ability to capture local features of occluded targets.At the same time,BAM focuses on both spatial and channel attention,effectively filtering environmental noise and reducing the influence of environmental interference on model detection.Experimental results show that compared to the baseline algorithm,the improved algorithm increases accuracy by0.2 percentage points,recall by 2.1 percentage points,and mAP@0.5 by 2.6 percentage points.The model size is only 2.0 M,and the FPS reaches 84.5 frames.关键词
安全帽检测/YOLOv8n/特征融合/改进小目标层/注意力机制Key words
helmet detection/YOLOv8n/feature fusion/improved small target layer/attention mechanism分类
信息技术与安全科学引用本文复制引用
盛鹏,张敏,晋从乾,朱子玄,江文豪..基于YOLOv8n的施工场景下安全帽佩戴检测算法[J].计算机技术与发展,2025,35(3):34-39,6.基金项目
安徽省高校优秀科研创新团队项目(2022AH010095) （2022AH010095）
合肥大学人才基金项目(20RC19) （20RC19）
