摘要
为了提高道路交通标志的检测速度,提出一种基于轻量化YOLOv5的改进模型。首先,使用Ghost卷积和深度分离卷积(DWConv)构建新的主干模块,减少计算量和参数量;引入加权特征融合网络(BiFPN)结构,增强特征融合能力;将CIoU损失函数替换为SIoU损失函数,关注真实锚框与预测的角度信息,提升检测精度。其次,对TT100K数据集进行优化,筛选出标签个数大于200的交通标志图片和标注信息共24类。最后,实验结果取得84%的准确率、81.2%的召回率和85.4%的所有类别平均精确率的平均值mAP@0.5,相比原始YOLOv5,参数量减少29.0%,计算量减少29.4%,mAP@0.5仅下降0.1百分点,检测帧率提升了34帧/s。使用改进后的模型进行检测,检测速度有了明显提升,基本达到了在保持检测精度的基础上压缩模型的目的。
In order to improve the detection speed of road traffic signs,an improved model based on lightweight YOLOv5 was proposed.Firstly,Ghost convolution and depthwise convolution were used to build a new Bottleneck,which could reduce the amount of computation and parameters.Then the BiFPN structure was introduced,which could enhance the feature fusion ability.CIoU loss function was replaced by SIoU loss function,which focused on the angle information of ground true box and prediction one,so that it would improve the detection accuracy.Secondly,the TT100K dataset was optimized,and 24 categories of traffic sign pictures and labels with more than 200 were screened out.Finally,the experiment achieved 84%accuracy,81.2%recall and 85.4%mAP@0.5.Compared with the original YOLOv5 model,the number of parameters was reduced by 29.0%,the amount of computation was reduced by 29.4%,but the mAP@0.5 was only reduced by 0.1 percentages,and the detection frame rate was improved by 34 frames/s.Using the improved model for detection,the detection speed could be significantly improved,could basically achieve the goal of compression model on the basis of maintaining the detection accuracy.
作者
张震
王晓杰
晋志华
马继骏
ZHANG Zhen;WANG Xiaojie;JIN Zhihua;MA Jijun(School of Electrical and Information Engineering,Zhengzhou University,Zhengzhou 450001,China;Henan Province Transportation Dispatching Command Center,Zhengzhou 450001,China)
出处
《郑州大学学报（工学版）》
CAS
北大核心
2024年第2期12-19,共8页
Journal of Zhengzhou University（Engineering Science）
基金
国家重点研发计划重点专项(2018XXXXXXXX03)
河南省交通运输厅科技项目(2019G3)。
