基于改进SSD的道路交通标志检测

针对复杂环境下交通标志检测精度低的问题,设计了一种检测精度更高的目标检测算法,对SSD深度学习目标检测算法进行了优化改进;将深度特征表征能力较强的Resnet50网络模型融入于SSD算法中;采用K-means++聚类算法确定SSD中先验框的尺寸,提高交通标志的检测率;分别利用SSD模型和改进的SSD模型做检测对比实验,结果表明,改进算法对各类型交通标志的检测精度比原SSD算法更高;改进的SSD方法对交通标志进行检测能取得较好效果,弥补了原算法的不足.

Road traffic sign detection based on improved SSD

Traffic sign detection has important applications in the field of unmanned driving. Aiming at the problem of low detection accuracy of traffic signs in complex environments, this paper optimizes and improves the SSD algorithm, and proposes to replace the feature extraction network from VGG16 with Resnet50 with stronger feature extraction capabilities. In order to improve the detection effect, this paper uses the K-means++ clustering algorithm Determine the size of the a priori box in the SSD. Based on the TensorFlow deep learning framework, this paper locates and classifies traffic signs in images in complex environments, and uses the SSD model and the improved SSD model to perform detection and comparison experiments, and analyze the model test results. The results show that the improved method has higher detection accuracy for various types of traffic signs than the original SSD algorithm. The method in this paper can achieve better results in detecting traffic signs.