| 引用本文: | 杨琚钱,胡 平,戴家树.基于 Yolov8-SCG 神经网络的电动车头盔佩戴检测算法(J/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2026，43（2）：101-107 |
| CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435 |
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| 摘要: |
| :目的 针对行人与电动车驾驶人员共用道路通行情况下,电动车骑行人员的头盔检测问题,提出一种基于
Yolov8n 的电动车头盔佩戴检测改进算法。 方法 首先,为提升模型对于低分辨率图像及小目标检测的精度,引入
SPDConv 替换模型普通卷积与下采样算子;其次,为提升模型对于背景与检测目标相似情况下的辨识能力,引入
CG block 模块,与原模型 C2f 模块融合,创建 C2f-CG 模块,替换原有 C2f 模块,提升上下文特征提取能力;最后,为
降低模型的计算量,保持模型轻量化,将原模型 Head 层普通卷积替换为组卷积。 结果 经实验检测,改进模型较
Yolov8n 原模型 mAP 精度提升 4. 2%,计算量降低 15%,对于低分辨率图像及小目标检测精度均有上升。 结论 改进
模型适用于复杂情况下电动车头盔的实时检测,可以作为应用领域的解决方法。 |
| 关键词: 电动车头盔检测 YOLOv8n SPDConv CG block GroupConv |
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| Electric Vehicle Helmet Wearing Detection Algorithm Based on Yolov8-SCG Neural Network |
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YANG Juqian HU Ping DAI Jiashu
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School of Computer and Information Anhui Polytechnic University Wuhu 241000 Anhui China
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| Abstract: |
| Objective In the scenario where pedestrians and electric vehicle drivers share the road an improved electric
vehicle helmet-wearing detection algorithm based on Yolov8n is proposed to address the helmet detection of electric vehicle
riders. Methods First to improve the model?? s accuracy in detecting low-resolution images and small targets SPDConv
was introduced to replace the ordinary convolution and down-sampling operators in the model. Second to enhance the
model?? s ability to distinguish objects when the background was similar to the detection target the CG block module was
introduced and integrated with the original C2f module to create the C2f-CG module which replaced the original C2f
module to boost the context feature extraction capability. Finally to reduce the computational volume of the model and
keep the model lightweight the ordinary convolution in the Head layer of the original model was replaced with group
convolution. Results After experimental testing the enhanced model proposed in this paper demonstrated an
improvement in mAP accuracy of 4. 2% and a reduction in computation of 15% in comparison to the Yolov8n original
model. The detection accuracy for low-resolution images and small targets had also increased. Conclusion The improved
model is suitable for electric vehicle helmet detection in complex situations and can be used as a solution in practical
fields |
| Key words: electric vehicle helmet detection YOLOv8n SPDConv CG block GroupConv |
