System and method for image search through images of multi-task portal vehicles

A multi-task and vehicle technology, which is applied in the field of multi-task bayonet vehicle image search, can solve the problems of not reaching the level of fine-grained vehicle retrieval and not being accurate enough.

Active Publication Date: 2018-06-15
ZHEJIANG YINJIANG RES INST
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AI Technical Summary

Problems solved by technology

[0005] "Deep Learning-Based Vehicle Vehicle Identification Model Construction Method and Vehicle Vehicle Identification Method", application number 20161096

Method used

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  • System and method for image search through images of multi-task portal vehicles
  • System and method for image search through images of multi-task portal vehicles
  • System and method for image search through images of multi-task portal vehicles

Examples

Experimental program
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Embodiment

[0101] Example: such as figure 1 As shown, a multi-tasking bayonet vehicle image search system mainly includes four modules, namely positioning module, feature extraction module, index module and image upload module, wherein the positioning module and feature extraction module both include the bayonet The treatment of the three parts of the vehicle, the annual inspection label and the headlights. The combination of the extracted feature vectors of the three parts is used as the feature vector of the bayonet vehicle. After PCA dimension reduction, the improved weighted K-means retrieval algorithm is used for retrieval, and finally the retrieved similar vehicles are uploaded.

[0102] A method for searching images by images for a multi-task bayonet vehicle, comprising the following steps:

[0103] Step 1. Data set preparation:

[0104] (1) Manually mark the vehicle, the vehicle annual inspection standard, and the area coordinate information and category of the vehicle light posi...

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Abstract

The invention relates to a system and a method for image search through images of multi-task portal vehicles. According to the method, a deep neural network is utilized to establish a multi-task positioning network and a multi-task feature extraction network; the positioning network is trained based on an improved edge box detection technology and cascaded loss functions; positioning and feature detection are performed on vehicles, annual inspection signs and lamps in portal vehicle images, and global features and local features are combined; a softmax loss function and a triad loss function are adopted for network training; and finally local feature vectors are subjected to weighting combination, and global feature vectors of the last full-connection layer of the neural network are utilized to serve as vehicle features for retrieval, wherein an improved k-means algorithm is adopted to find a K class in retrieval, and then an SVM is utilized to form a Hash function for Hamming coding.In this way, retrieval speed is increased, and storage space is saved.

Description

technical field [0001] The invention relates to the field of intelligent transportation, in particular to a system and method for searching images by images for multi-task bayonet vehicles. Background technique [0002] With the development of society, intelligent traffic monitoring in the field of intelligent transportation is a very important development direction at present. At present, a large number of electronic police and checkpoint systems have been deployed on urban roads in my country. These systems can capture high-definition pictures of vehicles in real time, and identify and analyze the license plate number, as well as some vehicle model information (such as vehicle size, color, etc.). However, in the currently used bayonet monitoring system, the license plate number recognition still has a misrecognition and missed recognition rate of about 10%. More importantly, it will not be possible to identify illegal vehicles with license plates or deliberately concealed...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/08
CPCG06N3/08G06V20/584G06F18/23213G06F18/214
Inventor 温晓岳田玉兰田彦陈涛李建元
Owner ZHEJIANG YINJIANG RES INST
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