基于改进SSD的交通标志检测算法

Traffic sign detection algorithm based on improved SSD

为了解决真实交通场景下交通标志因目标较小而导致检测精度低的问题,提出了一种改进SSD的交通标志检测算法。首先使用更深层次的ResNest网络替换原始SSD算法的主干网络VGG16来增强弱目标特征的强表征能力,然后在SSD的额外添加层使用RFB模块来增加小目标的感受野。其次使用Bi-FPN加权双向特征金字塔网络有效结合深层与浅层的特征信息,改善小目标的检测性能。最后使用K-means++聚类算法调整默认窗口的大小,有效避免因原始默认窗口太大但交通标志较小而无法匹配的问题,以改善检测效率。实验结果表明,本文提出的模型在中国交通标志数据集(CCTSDB)上获得了95.33%的mAP,与原始SSD模型相比,本文所构建的模型能更好的适应自然背景下的交通标志检测。

In order to solve the problem of low detection accuracy of traffic signs due to small targets in real traffic scenes,a traffic sign detection algorithm based on improved SSD was proposed.First,a deeper ResNest network was used to replace VGG16,the backbone network of the original SSD algorithm,to enhance the strong characterization of weak target features.Then,the RFB module was used in the additional layer of SSD to increase the receptive field of small targets.Secondly,bi-FPN weighted bidirectional feature pyramid network is used to effectively combine deep and shallow feature information to improve the detection performance of small targets.Finally,K-means++ clustering algorithm was used to adjust the size of the default window,which effectively avoided the problem that the original default window was too large but the traffic signs were small and could not be matched,so as to improve the detection efficiency.Experimental results show that the proposed model achieves 95.33% mAP on the China Traffic Signs Data Set(CCTSDB).Compared with the original SSD model,the proposed model can better adapt to traffic signs detection under natural background.