A vehicle type identification method and system based on automatic amplified samples

A vehicle recognition and sample technology, applied in the field of target recognition and vehicle recognition, can solve the problems of limited recognition rate and low recognition rate, and achieve the effect of solving difficult training.

Active Publication Date: 2022-03-29
HEFEI UNIV OF TECH
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Problems solved by technology

However, in the case of insufficient samples, the recognition rate of this system is limited to a certain extent, and the recognition rate is not high.

Method used

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  • A vehicle type identification method and system based on automatic amplified samples
  • A vehicle type identification method and system based on automatic amplified samples
  • A vehicle type identification method and system based on automatic amplified samples

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Embodiment Construction

[0039] The present invention will be further described below in conjunction with specific embodiments.

[0040] The method described is as figure 1 As shown, the steps include:

[0041] S1: Preprocessing and labeling of vehicle images:

[0042] Control the camera device to take pictures of the vehicle in motion, manually mark and preprocess all the obtained images, mark the position and size of the bounding box where the vehicle is located, and the type of the vehicle. Normalize the size of all images to 448×448, and perform mirroring, averaging, centering, and random flipping on all images before training to increase the number of images. If there is a single-channel image, it is changed to a three-channel image.

[0043] S2: Training of the sample generation module:

[0044] Normalize the vehicle image in the bounding box in S1 to a size of 256×256, and input it into the generation confrontation network. like figure 2 As shown, the generative adversarial network consi...

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Abstract

The invention provides a vehicle type identification method and system based on automatic amplified samples. The steps of the vehicle identification method are as follows: firstly, the vehicle image is preprocessed and marked; then the vehicle detection network is trained; at the same time, the sample generation module based on the generative confrontation network is used to generate new vehicle image data, which is the generated sample, and the generated sample is automatically Annotate, train the model recognition network; finally deploy the online vehicle detection network and model recognition network. The system includes a vehicle image preprocessing and labeling module, a vehicle detection module, a sample generation module, an automatic labeling module for generated samples and a vehicle type identification module. The car model recognition method solves the problem that the car model recognition rate is not high for vehicle images taken from various angles in the case of insufficient samples.

Description

technical field [0001] The invention relates to the technical fields of target recognition and vehicle recognition, in particular to a method and system for recognizing a vehicle model based on automatic sample amplification. Background technique [0002] In today's society, with the improvement of people's living standards and the accelerated development of urbanization, the number of vehicles per capita increases accordingly, and the resulting traffic and social problems are also becoming more and more prominent. Vehicle recognition technology is an important branch of computer vision and intelligent transportation. It has a wide range of applications in the fields of analyzing traffic flow, regulating traffic order, parking lot fee management, bayonet system, traffic accident detection and combating stolen vehicles. [0003] The application date is December 30, 2014, the publication date is July 19, 2019, and the authorized announcement number is CN105809088B for the Chin...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06V20/54G06N3/04G06N3/08
CPCG06N3/08G06V20/54G06V20/52G06V2201/08G06N3/045
Inventor 余烨杨昌东路强陈维笑程茹秋
Owner HEFEI UNIV OF TECH
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