Vehicle model identification method based on convolutional neural network

A convolutional neural network and vehicle recognition technology, applied in biological neural network models, neural learning methods, neural architectures, etc., can solve the problems of blurred vehicle recognition lighting images, low vehicle recognition accuracy, and impact on vehicle recognition accuracy. Improve vehicle recognition accuracy, improve efficiency, and reduce the effect of overfitting

Inactive Publication Date: 2019-12-20
NORTHEASTERN UNIV
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Problems solved by technology

However, in the actual traffic environment, there are factors such as illumination changes and image blurring in vehicle recognition, which affect the accuracy of convolutional neural network for vehicle recognition.
To sum up, facing the problem of relatively low recognitio

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  • Vehicle model identification method based on convolutional neural network
  • Vehicle model identification method based on convolutional neural network
  • Vehicle model identification method based on convolutional neural network

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[0053] The specific embodiments of the present invention will be described in further detail below in conjunction with the drawings and embodiments. The following examples are used to illustrate the present invention, but not to limit the scope of the present invention.

[0054] Such as figure 1 As shown, the method of this embodiment is as follows.

[0055] Step 1: Collect a data set of vehicle pictures, use data augmentation to amplify the data set to get pictures closer to the real scene, and divide the amplified data set into a training set and a test set according to a certain proportion;

[0056] The model picture data set used in this embodiment is the BIT-vehicle data set, with a total of 9,850 pictures, including 6 types of passenger cars, vans, mini cars, cars, SUVs, and trucks;

[0057] Among them, the two strategies of rotation and Gaussian blur are used to process each image in the BIT-vehicle model image data set, and the processed image is added to the original model im...

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Abstract

The invention discloses a vehicle model identification method based on a convolutional neural network, and belongs to the technical field of vehicle model identification in intelligent transportation.According to the method, a data set is amplified by adopting a data enhancement strategy, so that the data set is more suitable for the actual situation, the weight and the offset are updated by adopting an Adam optimization algorithm for vehicle model identification on the basis of an Inception-V3 network, and the efficiency of vehicle model identification is improved by referring to a transferlearning thought. Besides, the AMSoftmax loss function is adopted to replace the traditional Softmax loss function on the basis of the loss function, so that the intra-class difference is reduced, theinter-class difference is expanded, and the vehicle model identification precision in different background environments is improved.

Description

technical field [0001] The invention relates to the technical field of vehicle identification in intelligent transportation, in particular to a vehicle identification method based on a convolutional neural network. Background technique [0002] Due to the increasing traffic pressure in recent years, the importance and urgency of the intelligent transportation system has become increasingly evident. As an important branch and key technology of the intelligent transportation system, vehicle type recognition is mainly for accurate identification of vehicles in specific locations, which can provide Provide technical support for intelligent parking lot charging system, vehicles illegally occupying roads, etc. [0003] The main methods of vehicle recognition research at home and abroad can be summarized as: methods based on image processing and methods based on physics. Convolutional neural network is a kind of neural network. It consists of convolutional layer, pooling layer, ac...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08G06F16/55
CPCG06N3/08G06F16/55G06V2201/08G06N3/045G06F18/214
Inventor 赵宏阳付俊井元伟
Owner NORTHEASTERN UNIV
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