Multi-vehicle target identification method based on improved YOLOv2 model

A target recognition, multi-vehicle technology, applied in the field of image detection and classification, can solve the problems of poor robustness, low detection rate, and unsatisfactory classification effect.

Pending Publication Date: 2021-01-26
XI'AN POLYTECHNIC UNIVERSITY
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Problems solved by technology

[0004] The purpose of the present invention is to provide a multi-vehicle target recognition method that improves the YOLOv2 model, which solves the problems of ...

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  • Multi-vehicle target identification method based on improved YOLOv2 model
  • Multi-vehicle target identification method based on improved YOLOv2 model
  • Multi-vehicle target identification method based on improved YOLOv2 model

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Embodiment Construction

[0061] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0062] The present invention improves a multi-vehicle target recognition method of the YOLOv2 model, and the flow chart is as follows figure 1 As shown, the specific steps are as follows:

[0063] Step 1, collect the sample data under the actual traffic environment, and divide the sample data into the sample images of the training set and the test set with a ratio of 7:3;

[0064] Step 1 is as follows:

[0065] Step 1.1, shoot the vehicle information under the real-time road traffic environment, extract the captured video frame into image format and delete the pictures with poor image quality;

[0066] Step 1.2. Use the LabImage labeling tool to label the vehicles in the selected picture, frame the target area, and classify the vehicles in the target area and make labels. The labels are car, bus, van, truck, and each picture generates a .xm...

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Abstract

The invention discloses a multi-vehicle target identification method based on an improved YOLOv2 model, and the method comprises the steps: firstly collecting sample data in an actual traffic environment, and dividing the sample data into sample images of a training set and a test set according to a proportion of 7: 3; then performing data enhancement on the training set sample images, including random scaling of the sample images and adjustment of exposure and saturation so that the processed images are enabled to act as the input of the training model; carrying out target area feature vectorextraction on the processed training set through an improved Darknet-19 network; inputting the training set into a Darknet-19 network model for training to obtain a detection and recognition model; and finally, inputting the test set into the model for testing to obtain a multi-target vehicle identification result. According to the method, the problems of low detection rate, poor robustness and non-ideal classification effect of a road vehicle multi-target detection and vehicle type classification method in the prior art are solved.

Description

technical field [0001] The invention belongs to the technical field of image detection and classification, and in particular relates to an improved YOLOv2 model multi-vehicle target recognition method. Background technique [0002] Image detection and image classification technology is an important part of image processing technology, which is widely used in many fields, such as remote sensing image recognition, military criminal investigation, modern biomedicine, intelligent transportation, etc. However, traditional target detection and recognition methods, such as the Cascade classifier based on Haar features, are mainly for the detection of specific targets, which are limited to multi-category targets, and the process of target area selection is complicated, and the efficiency of detection and recognition is low. When selecting objects, its feature extraction has strong subjectivity, poor robustness, weak generalization ability and other shortcomings, and it is difficult ...

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62G06N3/04G06N3/08G06T3/40G06T7/11
CPCG06T3/40G06T7/11G06N3/084G06V20/54G06V10/44G06V2201/08G06V2201/07G06N3/045G06F18/23213G06F18/214
Inventor 李珣时斌斌聂婷婷张玥李林鹏贠鑫
Owner XI'AN POLYTECHNIC UNIVERSITY
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