Traffic sign board recognition method and device, computer equipment and storage medium

Abstract

The invention discloses a traffic sign board recognition method and device and computer equipment. The recognition method comprises the steps of acquiring a gray level image of a to-be-recognized image, generating an image pyramid according to the gray level image, the image pyramid comprising a plurality of images, and the sizes of the images are different; for the image of each size, traversingthe image of each size according to the target detection window and the target sliding step length, and obtaining an N-dimensional gradient direction feature vector of the image of each size; and classifying the N-dimensional gradient direction feature vector of the image of each size through a pre-trained signboard detection model to detect whether a traffic signboard exists in the original imageor not. According to the method, the detection and recognition speed of the traffic sign board can be increased, and the detection and recognition rate is increased.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to a traffic sign recognition method, device, computer equipment and computer-readable storage medium. Background technique [0002] With the development of the economy, modern transportation has been very developed, while road traffic still occupies a dominant position. However, traffic safety and traffic congestion have become increasingly serious social problems, and have also caused staggering economic losses. Coupled with environmental pollution and energy issues, the solution to road traffic problems has to resort to intelligent technology. The research field of intelligent transportation system came into being and developed rapidly. [0003] Road traffic sign recognition is one of the unresolved problems in the field of intelligent transportation system research, and it is also one of the more difficult real-scene graphics recognition problems. In related ...

AI Technical Summary

Problems solved by technology

However, the existing target detection algorithm based on deep learning has a deep network hierarchy, the total number of parameters can reach tens of megabytes, the calculation time is long, and it does not meet the real-time requirements; while the features extracted by traditional target classification methods are manually designed , has certain limitations, is easily affected by factors such as illumination, size, deformation and shooting angle, and has a low detection rate

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