Detection and recognition method of monocular vision traffic safety information for intelligent vehicles

Abstract

Intelligent vehicle technology has become a research hot issue in recent ten years, the reason is that intelligent vehicles can not only be used as a flexible weapon platform in the military. And in life, it is also a system that provides convenience and security for people. For example, driverless cars and advanced driver assistance systems (ADAS). Information processing is the key to the degree of intelligence, and the detection and recognition of traffic safety information based on monocular vision is the core of information processing, it’s also the bottleneck problem. Because of the complexity and diversity of the environment have brought great challenges to this problem. In this paper, the existing lane detection methods in structured and semi-structured roads do not specifically consider the problem of weak line detection, two models are proposed. Fuzzy LDA enhancement model is used to enhance the contrast of lane area, another brightness contrast saliency model can be used for robust Lane extraction. Then, two models are applied to lane detection, a two-stage lane detection method is proposed and a blind area vehicle detection method is designed. Firstly, the vehicle area is roughly extracted based on road gray statistics, and then the typical vehicle features are screened finely. Finally, the extracted features and SVM classifiers are used to confirm the candidate regions. Experiments show that: The proposed method can detect the vehicle in the blind area very well and is insensitive to the shape distortion and size change of the vehicle.