Abstract
A vehicle target detection and information extraction scheme based on NI (National Instruments) myRIO is designed in this paper. The vehicle information acquisition and processing method based on image recognition is used to design a complete vehicle detection and information extraction system. In the LabVIEW programming environment, the edge detection method is used to realize the vehicle target detection, the pattern matching method is used to realize the vehicle logo recognition, and the Optical Character Recognition (OCR) character recognition algorithm is used to realize the vehicle license plate recognition. The feasibility of the design scheme in this paper is verified through the actual test and analysis. The scheme is intuitive and efficient, with the high recognition accuracy.
Highlights
In the current intelligent transportation system (ITS), the accuracy and the efficiency of the real-time vehicle targets detection and vehicle identity recognition become more important with the increasing number of vehicles and the increasingly complex traffic environment [1,2]
(Optical Character Recognition)-based license plate recognition using neural network trained dataset of object features is proposed and is compared with the existing methods for improving accuracy in [15]
Platform to realize vehicle target detection based on edge detection, vehicle color recognition based hardware platform to realize vehicle target detection based on edge detection, vehicle color on color classification function, vehicle logo recognition based on pattern matching, and license plate recognition based on color classification function, vehicle logo recognition based on pattern recognition based on Optical Character Recognition (OCR) character recognition algorithm
Summary
In the current intelligent transportation system (ITS), the accuracy and the efficiency of the real-time vehicle targets detection and vehicle identity recognition become more important with the increasing number of vehicles and the increasingly complex traffic environment [1,2]. Traffic information monitoring technology based on image recognition has become a hot spot in the transportation field in recent years. It collects traffic video by the camera, and uses image recognition and processing technology to identify and process the target area, monitor the traffic condition, and extract traffic information through a certain algorithm, which has the advantages of convenient installation, wide coverage, and high detection accuracy. The edge detection method based on Canny operator is used in this paper It has a small amount of calculation and high recognition accuracy, and can effectively remove noise and improve recognition reliability [8]
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