We can create a model that categorizes traffic signs and let the model learn to identify the most critical features of these traffic signs. In this article, I will demonstrate how to create a deep learning architecture that has a recognition accuracy of 98% on traffic sign test sets.
Traffic signs are an important part of road infrastructure, providing road users with some key information and requiring drivers to adjust their driving behavior in a timely manner to ensure compliance with road safety regulations. If there is no traffic sign, more accidents may occur because the driver cannot know the maximum safety speed and does not understand the road conditions, such as sharp turns, school crossings, and so on. Today, about 1.3 million people die every year on the road. If you don't have these road signs, this number will definitely be higher.
Of course, autonomous vehicles must also comply with traffic regulations, so it is necessary to identify _ and _ understand _ traffic signs.
Traditionally, standard computer vision methods can be used to detect and classify traffic signs, but it also takes considerable time to manually process important features in the image. Now we introduce deep learning techniques to solve this problem. We can create a model that categorizes traffic signs and let the model learn to identify the most critical features of these traffic signs. In this article, I will demonstrate how to create a deep learning architecture that has a recognition accuracy of 98% on traffic sign test sets.
Project settingData sets can be divided into training sets, test sets, and verification sets, with the following characteristics:
The image is 32 (wide) & TImes; 32 (high) & TImes; 3 (RGB color channel)
The training set consists of 34,399 pictures
The verification set consists of 4410 images
The test set consists of 12,630 images
There are 43 types (such as speed limit 20 km / h, no entry, bumpy road, etc.)
In addition, we will use Python 3.5 and Tensorflow to write code.
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