Helmet-YOLO:一种更高精度的道路安全头盔检测算法  

Helmet-YOLO:Higher-Accuracy Road Safety Helmet Detection Algorithm

作　　者：周顺勇 彭梓洋 张航领 胡琴 陆欢 张宗良 ZHOU Shunyong;PENG Ziyang;ZHANG Hangling;HU Qin;LU Huan;ZHANG Zongliang(School of Automation and Information Engineering,Sichuan University of Science&Engineering,Yibin,Sichuan 644000,China;Key Laboratory of Artificial Intelligence in Sichuan Province,Yibin,Sichuan 644000,China)

机构地区：[1]四川轻化工大学自动化与信息工程学院,四川宜宾644000 [2]人工智能四川省重点实验室,四川宜宾644000

出　　处：《计算机工程与应用》2025年第2期135-144,共10页Computer Engineering and Applications

基　　金：国家自然科学基金(61801319);四川省科技厅项目(2020YFSY0027);四川省大学生创新项目(S202310622099);四川轻化工大学科研项目(2023RC24)。

摘　　要：针对现有的道路安全头盔检测算法受背景环境影响较大,面对遮挡以及目标与环境相似等检测场景检测精度不高的问题,从特征融合和损失计算的角度,开发了一种新的Helmet-YOLO架构。利用渐进式特征金字塔网络结构降低多尺度特征融合过程中存在的巨大语义差距,提升算法在复杂场景下的检测能力。同时,提出的PCAHead检测头和HelmetIoU边界框损失函数优化了模型理解和处理数据的能力,提高了模型损失计算的效率和精度,加速了模型的收敛。实验结果表明,Helmet-YOLOn算法和Helmet-YOLOs算法的mAP@50分别提升了3.7和2.9个百分点,优于实验中的所有同尺度模型,另外Helmet-YOLO的大尺度模型在延迟和精度上也优于多数实验模型。实验证明Helmet-YOLO算法具有更高的精度和鲁棒性,更适合复杂场景的道路安全头盔检测。In view of the significant influence of background environment on the existing road safety helmet detec-tion algorithm and the issue of low detection accuracy in detection scenarios such as occlusion and similarity between tar-gets and the environment,this paper considers the YOLOv8 model from the perspectives of feature fusion and loss calcu-lation.By utilizing the progressive feature pyramid network structure to reduce the substantial semantic gap in the process of multi-scale feature fusion,the algorithm’s detection capability in complex scenes is enhanced.Additionally,the pro-posed PCAHead for detection and HelmetIoU bounding box loss function optimize the model’s understanding and data processing capabilities,improving the efficiency and accuracy of model loss calculation,thereby accelerating model con-vergence.Experimental results show that the mAP@50 of the Helmet-YOLOn algorithm and Helmet-YOLOs algorithm have increased by 3.7 percentage points and 2.9 percentage points,respectively,outperforming all models of the same scale in the experiment.Furthermore,the large-scale model of Helmet-YOLO also outperforms most experimental models in terms of latency and accuracy.The experiments demonstrate that the Helmet-YOLO algorithm has higher accuracy and robustness,making it more suitable for road safety helmet detection in complex scenarios.

关 键 词：头盔检测 Helmet-YOLO 渐进式特征金字塔网络 PCAHead检测头 HelmetIoU 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]