Pedestrian identification method under road traffic environment based on improved YOLOv3.

A technology for road traffic and pedestrian recognition, applied in the field of pedestrian recognition in the road traffic environment based on improved YOLOv3, which can solve problems such as poor robustness and large amount of calculation, and achieve the effects of increasing detection speed, improving positioning accuracy, and ensuring detection accuracy

Inactive Publication Date: 2019-02-12
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

The recognition method based on DPM needs to establish local models of multiple ped...

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  • Pedestrian identification method under road traffic environment based on improved YOLOv3.
  • Pedestrian identification method under road traffic environment based on improved YOLOv3.
  • Pedestrian identification method under road traffic environment based on improved YOLOv3.

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Embodiment Construction

[0040] In order to show the advantages and implementation modes of the present invention more clearly, the present invention will be further elaborated in conjunction with the accompanying drawings.

[0041] figure 1 It is a flow chart of the overall implementation process of a pedestrian recognition method based on improved YOLOv3 in the road traffic environment of the present invention, figure 2 It is the network structure diagram of YOLOv3, and Figure (3) is the flow chart of the candidate frame clustering algorithm, combined with figure 1 , figure 2 and image 3 , the steps of the specific embodiment of the present invention are:

[0042] A pedestrian recognition method based on improved YOLOv3 in road traffic environment, comprising the following steps:

[0043] S1. Pedestrian image acquisition and preprocessing under different road traffic environments, and making pedestrian sample sets under road traffic environments;

[0044] S2. Based on the training set, use t...

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Abstract

The invention discloses a pedestrian identification method under a road traffic environment based on improved YOLOv3. The method comprises the following steps of: S1, acquiring and pre-processing an image, and making a pedestrian sample set; 2, calculating the length-width ratio of the pedestrian candidate frames by using a clustering algorithm and the training set; 3, inputting the training set into the YOLOv3 network for multi-task training and saving the trained weight file; S4, inputting a picture to be recognized into the YOLOv3 network to obtain a multi-scale characteristic map; S5, using a logistic function to activate the x, y, confidence degree and category probability of the network prediction, and obtaining the coordinates, confidence degree and category probability of all prediction frames by judging the threshold value; S6, generating a final target detection frame and a recognition result by carrying out the non-maximum value suppression processing on the above result. The method of the invention solves the problem of low detection accuracy of the prior method, realizes the multi-task training, does not need additional storage space, and is high in detection accuracyand fast in speed.

Description

technical field [0001] The invention belongs to the field of automobile safety assisted driving and image processing, and more specifically relates to a pedestrian recognition method based on improved YOLOv3 in road traffic environment, which can be used to solve the problems of low pedestrian recognition accuracy and long detection time in road traffic environment . Background technique [0002] Pedestrian recognition in the road traffic environment has always been a difficult point in computer vision research due to the complexity of the traffic environment and the variability of pedestrian postures. As an important component of the road environment, pedestrians can be identified accurately, quickly and effectively to better assist people in driving. [0003] Commonly used pedestrian detection methods mainly include the method based on LBP, Haar, HOG and other features, the method of establishing a deformable part model (Deformable Part Model, DPM) combined with a classif...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/10G06V20/56G06F18/23213G06F18/24G06F18/214
Inventor 李巍华方卓琳刘晓楠
Owner SOUTH CHINA UNIV OF TECH
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