Traffic sign detection method based on convolutional neural network

Abstract

The invention relates to a traffic sign detection method based on a convolutional neural network. The method comprises the following steps: step 1, constructing a traffic sign detection network with classification and positioning separated based on the convolutional neural network; 2, in the training stage, training the constructed traffic sign detection network by adopting an enhanced iterative training method to obtain a traffic sign detection model; and step 3, in the use stage, carrying out target detection on the input image by adopting a separation and fusion prediction method to obtaina traffic sign detection result. According to the method, rapid and accurate traffic sign detection is realized in a complex traffic monitoring scene, the robustness to the environment is high, and the detection accuracy for small-size traffic signs is relatively high.

Description

technical field

[0001] The invention relates to the technical fields of computer vision and intelligent transportation, and more specifically, to a traffic sign detection method based on a convolutional neural network, which can be applied to traffic sign detection in complex traffic street scenes. Background technique

[0002] With the rapid development of science and technology, various systems in the field of intelligent transportation are becoming more and more perfect, providing people with a more convenient and safe urban transportation system. As a part of the intelligent transportation system, traffic sign detection provides predictive information of traffic signs for intelligent driving, and then assists drivers in driving. It is one of the foundations of intelligent transportation systems. At present, traffic sign detection algorithms are mainly divided into three categories: traditional image detection algorithms, artificial feature detection algorithms, and deep ...

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