Abstract
With the progress of society and the rapid development of economy, the per capita car ownership increases year by year, which greatly meets people's travel needs. However, the increasing number of cars has brought great pressure to urban road traffic, and has also increased the risk of traffic safety accidents. Besides, drivers' misreading or unawareness of traffic signs is also an important reason for traffic accidents. In order to reduce the incidence of traffic accidents and increase the safety factor of traffic, the Intelligent Traffic System (ITS) arises at the historic moment. As an important part of ITS, traffic sign detection has also been paid great attention to. It can feed the road information to drivers or unmanned vehicles to avoid traffic accidents in driving and unmanned vehicles. It has a broad prospect of development and application. Therefore, the research of traffic sign detection is of great significance and practical value.
In this thesis, two methods are proposed for the detection of traffic signs in complex environments. They are a traffic sign detection method based on color and shape, and a traffic sign detection method based on deep learning. The traffic sign detection method based on color and shape is mainly aimed at circular, triangular and rectangular traffic signs. This method requires pre-processing of the image to obtain a higher-visualized image in order to achieve the requirement of color segmentation. Image preprocessing includes image filtering, defogging algorithm research, and removing the effects of uneven lighting. After the image is preprocessed, the RGB difference method is used to segment the region of interest into the image and put it into the candidate region for preliminary screening, that is, to perform morphological processing and other steps on the binary image of the region of interest of the candidate region. Remove the binary image that does not meet the shape features of traffic signs, and then perform shape detection on the remaining images to finally locate traffic signs. The maximum detection rate of this algorithm in this paper is 87.73%.
The traffic sign detection method based on deep learning is based on the classical convolutional neural network structure, and a convolutional neural network with traffic sign detection function is designed. Through the pre-training CIFAR-10 data set, a large number of low-level image features are learned through the network. At the same time, in order to better
optimize the network structure, local modifications and the import of traffic sign data sets are carried out for migration learning. Finally, the training has the function of detecting traffic signs. Convolutional neural network. The maximum detection rate of this algorithm in this paper is 97.48%, which has reached a high level compared with the traditional method.
Keywords: Intelligent traffic system, traffic sign detection, image preprocessing, deep learning, convolution neural network
III