License plate detection model based on improved YOLOv3 network and construction method

Abstract

The invention discloses a license plate detection model based on an improved YOLOv3 network and a construction method. The improved YOLOv3 network is used for inputting a license plate image and extracting three feature maps of different scales; carrying out up-sampling on the obtained three feature maps with different scales, scaling depth features to the same proportion, then carrying out down-sampling, and carrying out decoding through a constructed convolution layer to generate a feature map after feature enhancement; performing feature aggregation on the generated feature maps of the three different scales after feature enhancement and the feature maps of the three different scales extracted from the YOLOv3 feature extraction network to generate a feature pyramid, and obtaining an improved license plate detection model of the YOLOv3 network; and training the license plate detection model to obtain a final model. According to the method, the detection speed is greatly improved, thepyramid multi-scale feature network is introduced to enhance the features of the backbone network and generate a more effective multi-scale feature pyramid, and the features are better extracted fromthe input image.

Description

technical field [0001] The invention belongs to the field of computer vision, in particular to a license plate detection model and construction method based on an improved YOLOv3 network. Background technique [0002] With the rapid development of economy and society and the improvement of people's living standards, the number of motor vehicles has increased dramatically. In order to improve the management efficiency of vehicles and relieve the traffic pressure on the highway, we must find a solution. As the license plate can uniquely determine the identity of the car, solving the detection of the license plate in road traffic can greatly improve the safety management level and management efficiency of the car. [0003] In the traditional license plate detection, the image containing the license plate number is analyzed and processed through graphic segmentation and image recognition theory, so as to determine the position of the license plate in the image. However, the po...

AI Technical Summary

Problems solved by technology

However, the existing technology applies the YOLOv3 network to license plate detection. Due to the wide variety of detections implemented by the YOLOv3 network, the detection of a single target is complex and redundant. Too many parameters will lead to overly complicated training, which will affect the demand for data volume and training speed.

Images