Convolutional neural network based vehicle logo identification method

Abstract

The invention provides a convolutional neural network based vehicle logo identification method. The method is specifically implemented by the following steps of: (1) inputting a to-be-detected picture shot by a high-resolution camera device in a traffic intersection; (2) positioning a vehicle logo; (3) constructing and training a convolutional neural network; and (4) identifying the vehicle logo. With the adoption of the convolutional neural network (CNN) based vehicle logo identification method, the shortcomings of complicated extraction feature operator, poor timeliness and complicated model in the prior art can be effectively overcome, and the calculation amount is effectively reduced; and features of CNN self-learning have higher robustness on environmental change, so that the identification rate of the vehicle logo is increased.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a vehicle logo recognition method based on a convolutional neural network in the technical field of image-based pattern recognition. In the traffic system, the invention uses the vehicle pictures obtained by the high-definition camera equipment installed at the intersection to locate the vehicle logo, and then recognize the vehicle logo, so as to realize the automatic positioning and recognition of the vehicle logo. 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 fiel...

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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