基于SC-YOLOv8的交通标志检测算法研究

Research on traffic sign detection algorithm based on SC-YOLOv8

为了解决交通标志检测中所存在的准确率低、参数量大等问题,本文提出了一种基于YOLOv8s改进的SC-YOLOv8交通标志检测算法。该算法使用下采样Adown模块替换普通下采样Conv,提升模型对目标的感知能力;使用SCConv模块替换C2f中的Bottleneck,设计全新的C2f_SC模块,大幅减少模型参数;通过增加160×160尺度的检测头去除20×20尺度的检测头来改进目标检测层,有效的提高了检测精度;最后使用WIoU损失函数的思想改进MPDIoU,以Wise-MPDIoU替换原CIoU损失函数,缓解了正负样本不平衡的问题。该算法在TT100K交通标志数据集上进行验证,与原模型YOLOv8s进行比较,精确率P提升了4.8%,召回率R提升了6.7%,mAP50提升了6.6%,参数量Params下降了61.5%。证明了所做改进的有效性。

In order to solve the problems of low accuracy and large number of parameters in traffic sign detection,this paper proposes an improved SC-YOLOv8 traffic sign detection algorithm based on YOLOv8s.This algorithm uses the downsampling Adown module to replace the ordinary downsampling Conv,improving the model′s perception ability of the target;replace the Bottleneck in C2f with the SCConv module and design a brand new C2f-SC module,significantly reducing model parameters;adding a 160×160 scale detection head and removing a 20×20 scale detection head,effectively improving detection accuracy;finally,the idea of using WIoU loss function is used to improve MPDIoU,replacing the original CIoU loss function with Wise-MPDIoU,alleviating the problem of imbalanced positive and negative samples.The algorithm was validated on the TT100K traffic sign dataset,and compared with the original model YOLOv8s,the accuracy P increased by 4.8%,the recall R increased by 6.7%,the mAP50 increased by 6.6%,and the parameter count Params decreased by 61.5%.Proved the effectiveness of the improvements made.