Traffic sign recognition method based on deep learning

Abstract

A traffic sign recognition method based on deep learning belongs to the field of image recognition. The method comprises the following steps: step 1, acquiring a traffic sign data set, and preprocessing the data set; and step 2, constructing a TSR _ ConvNet network structure for training. And the TSR _ ConvNet network is added into a Dropout strategy for improving overfitting, and is added into abatch normalization layer. And designing a proper convolution kernel size. Wherein the softmax classification layer of the full connection layer adopts a Label-smoothing strategy, and the softmax classification layer of the full connection layer adopts a Label-softing strategy. And 3, inputting the preprocessed traffic sign image into a TSR _ ConvNet network model, and carrying out traffic sign identification. According to the method, the model structure is simplified under the condition of ensuring relatively high accuracy, so that the calculated amount of the model is small, the training time is greatly reduced, and the method has better practicability.

Description

technical field [0001] This technology belongs to the field of image recognition, and is a feature extraction based on convolutional neural network, which can be applied to the recognition of road traffic signs. Background technique [0002] Traffic signs contain a large amount of effective road information, which plays an important role in regulating traffic flow, relieving traffic congestion and predicting road conditions to prevent traffic accidents. However, the real natural environment is complex and changeable, and the accuracy and real-time performance of traffic sign recognition are easily affected by factors such as fading and deformation of traffic signs, complex lighting environment and weather changes, traffic signs blocked by obstacles, and blurred pictures caused by car movement. . Therefore, a fast and reliable traffic sign recognition system has become an important part of assisted driving and intelligent traffic systems. The existing traffic sign recogniti...

AI Technical Summary

Problems solved by technology

Although they can achieve high recognition accuracy, the amount of calculation in the training process is very large, the hardware requirements are high, the operation is complicated, and the training is very time-consuming

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