计算机科学 ›› 2021, Vol. 48 ›› Issue (11A): 345-350.doi: 10.11896/jsjkx.201200213
王济民, 魏怡, 周宇, 孙傲, 刘源升
WANG Ji-min, WEI Yi, ZHOU Yu, SUN Ao, LIU Yuan-sheng
摘要： 限速标志识别是智能驾驶的重要组成部分,文中分析了现有方法存在的问题,为了提高神经网络在中国限速标志上的泛用性和准确率,针对限速标志的检测部分,提出了一种基于颜色空间的新型筛选方法;针对限速标志的识别部分,在现有LeNet-5架构的基础上对神经网络进行了改进,并将德国交通标志数据集(GTSRB)和清华交通标志数据集(TT100K)中限速标志数据融合,经过数据扩增后制作成新的数据集送入神经网络来训练模型。通过多次超参数优化,采用swish激活函数,在测试集上得到的最优识别准确率为99.62%,且模型抗干扰能力强,具有较强的实用性能。
中图分类号:
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