吉林大学学报(工学版) ›› 2018, Vol. 48 ›› Issue (1): 319-329.doi: 10.13229/j.cnki.jdxbgxb20161120
王方石1, 王坚1, 李兵2, 3, 王博2, 3
WANG Fang-shi1, WANG Jian1, LI Bing2, 3, WANG Bo2, 3
摘要: 为了弥补交通标志底层图像到高层语义之间的鸿沟,本文引入交通标志的形状、颜色、图案内容三种视觉属性,在卷积神经网络(Convolutional neural network, CNN)中加入属性学习(Attribute learning)约束,同时进行交通标志属性学习和分类学习,提出了一种基于深度属性学习的交通标志检测方法。并在公开数据集Sweden traffic sign detection dataset(STSD)和German traffic sign detection dataset(GTSD)上进行的实验结果表明,该方法能够有效地提高交通标志检测的准确率和召回率。
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