Front vehicle information structured output method base on concatenated convolutional neural networks

Abstract

The present invention puts forward a front vehicle information structured output method base on concatenated convolutional neural networks, for mainly solving the problem that a traditional method cannot quickly detect and identify a vehicle body, a license plate and a vehicle logo in one time in a complex environment. The realization process of the front vehicle information structured output method comprises the steps of: 1, acquiring a sample set and selecting a vehicle body initial sample set from the sample set; 2, training the vehicle body initial sample set through a BING (Binarized Normed Gradients) method; 3, respectively training convolutional neural networks of vehicle body, license plate and vehicle logo; 4, judging the area and color of the vehicle body according to the well trained convolutional neural network of vehicle body; 5, judging the area of the license plate and identifying a license plate number according to the well trained convolutional neural network of license plate; 6, judging the area and class of the vehicle logo according to the well trained convolutional neural network of vehicle logo; and 7, outputting the all obtained information to a frame image. The front vehicle information structured output method of the present invention can accurately detect and identify front vehicle information in a complex environment, and can be used for intelligent monitoring, intelligent traffic, driver auxiliary and traffic information detection.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a method for structured output of vehicle information, which can be used for intelligent monitoring, intelligent transportation, driver assistance systems and traffic information detection. Background technique [0002] Vehicle detection and recognition in computer vision refers to obtaining vehicle information only by using the image input of the camera, which is a technology with very broad application prospects. The vehicle detection system based on computer vision has low hardware cost and can perceive rich environmental information, but it is greatly affected by environmental changes and is sensitive to changes in lighting conditions. Vehicle information detection and recognition has good application prospects in many fields, but due to the diversity of vehicles, background clutter, weather effects, lighting conditions, self-occlusion and other factors, ...

AI Technical Summary

Problems solved by technology

The disadvantage is that the resolution of the image to be detected is high, the positioning time is long, and the recognition rate is not high.

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