Selective search and convolutional neural network based vehicle logo recognition method

Abstract

The invention proposes a selective search and convolutional neural network based vehicle logo recognition method, and mainly solves the problems of complicated calculation and poor timeliness in the prior art. According to the implementation scheme, the method comprises the steps of 1) inputting a to-be-detected picture shot by a high-definition shooting device in a traffic intersection; 2) carrying out selective search for the to-be-detected picture to obtain candidate regions; 3) screening the candidate regions to obtain vehicle logo candidate regions; and 4) constructing and training a convolutional neural network (CNN) and inputting the logo candidate regions into the trained CNN for testing to obtain a vehicle logo recognition result. According to the method, the calculation amount is effectively reduced, the vehicle logo candidate regions can be quickly obtained, a self-learning characteristic of the CNN has higher robustness for environmental change, and the vehicle logo recognition rate is increased; and the method can be used for quick detection of freeway entrances and parking spaces to vehicles.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a vehicle logo recognition method, which can be used for rapid detection of vehicles at expressway entrances and parking lots. Background technique [0002] With the continuous improvement of the socio-economic level and the popularization of vehicles, the ever-expanding transportation industry has a greater demand for more intelligent technologies and systems, and intelligent transportation systems have become a hot issue in social life. As an important part of the intelligent transportation system, the vehicle identification system has a wide range of applications in the fields of highway entrance, unmanned parking lot management, and automatic recording of illegal vehicles. Its realization has great economic value and practical significance. [0003] Vehicle logo recognition is an important aspect of vehicle identification. Vehicle logo recognition technology r...

AI Technical Summary

Problems solved by technology

The disadvantages of this method are: First, because this method uses the HOG feature of the directional gradient histogram, the HOG descriptor generation process of the directional gradient histogram is lengthy, resulting in slow speed and poor real-time performance.

The disadvantage of this method is that in the positioning process, the strong classifier Adaboost algorithm based on the Haar feature and the support vector machine SVM algorithm based on the HOG feature of the direction gradient histogram are used. The support vector machine SVM algorithm of the HOG feature of the direction gradient histogram uses a total of three classifiers, which greatly increases the computational complexity

The disadvantage of this method is that due to the use of the dense scale invariant feature transformation dense-SIFT feature operator, the dimension is high, the calculation time is long, and the real-time performance is poor.

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