Vehicle type recognition method based on rapid R-CNN deep neural network

A technology of deep neural network and vehicle identification, applied in the field of vehicle identification based on fast R-CNN deep neural network, can solve the problems of reducing training and learning time

Active Publication Date: 2016-12-21
汤一平
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0020] To sum up, there are still several thorny problems in the recognition of vehicle models using deep neural network technologies such as convolutional neural networks: 1) How to accurately segment the overall image of the vehicle under test from the complex background; 2) How to use as little label image data as possible to accurately obtain the characteristic data of the vehicle model; 3) How to identify which model, which color, and which year the car was produced on the basis of the recognition of the vehicle model category; 4 ) How to automatically obtain the characte...

Method used

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Embodiment 1

[0120] refer to Figure 1-14 , the technical solution adopted by the present invention to solve its technical problems is:

[0121] (1) About designing a fast visual segmentation algorithm for vehicle objects;

[0122] First, a fast visual segmentation algorithm for vehicle objects is designed, that is, region selection and localization of vehicle objects are performed;

[0123] In order to locate the position of the vehicle target; since the vehicle target may appear in any position of the image, and the size and aspect ratio of the target are also uncertain, the original technology initially uses a sliding window strategy to traverse the entire image, And it is necessary to set different scales and different aspect ratios; although this exhaustive strategy includes all possible positions of the target, the disadvantages are also obvious: the time complexity is too high, and too many redundant windows are generated, which is also Seriously affect the speed and performance o...

Embodiment 2

[0246] The visual recognition technology of the present invention has universality and is suitable for subclass recognition of other objects. As long as the data participating in the training is run in the system developed by the present invention to learn, and the characteristics of this type of object can be obtained, the recognition of this type of object can be realized. Subclass recognition task.

Embodiment 3

[0248] The visual recognition technology of the present invention has expansibility. After a new subclass appears, it is not necessary to relearn and train the original trained network features, as long as the new subclass is trained and learned, and the softmax classifier in the system is extended categorized data.

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Abstract

The invention discloses a vehicle type recognition method based on a rapid R-CNN deep neural network, which mainly comprises unsupervised deep learning, a multilayer CNN (Convolutional Neural Network), a regional advice network, network sharing and a softmax classifier. The vehicle type recognition method realizes a framework for implementing end-to-end vehicle detection and recognition by using one rapid R-CNN network in a real sense, and is capable of carrying out quick vehicle sub-category recognition with high accuracy and robustness under the environment of being applicable to the shape diversity, the illumination variation diversity, the background diversity and the like of vehicle targets.

Description

technical field [0001] The invention relates to the application of computer technology, pattern recognition, artificial intelligence, applied mathematics and biological vision technology in the field of intelligent transportation, in particular to a vehicle identification method based on a fast R-CNN deep neural network. Background technique [0002] The core function of the intelligent transportation system is the accurate detection of passing vehicles and the correct identification of vehicle types. The current research on vehicle detection and classification technology mainly includes two important technologies: automatic vehicle identification and automatic vehicle classification. [0003] Automatic vehicle identification is carried out by mutual recognition between on-board equipment and ground base station equipment. This technology is mainly used in toll collection systems. It is widely used in some developed countries, such as the AE-PASS system in the United States,...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/08
CPCG06N3/08G06V20/584G06F18/24
Inventor 汤一平
Owner 汤一平
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