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

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...

AI Technical Summary

Problems solved by technology

The recognition method based on DPM needs to establish local models of multiple pedestrians, which is computationally intensive and less robust in complex road environments

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