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Traffic signs and rules are extremely crucial to observe to avoid any accidents. To observe the guideline, one must first comprehend the appearance of the traffic sign. Before receiving a driver’s license, a person must first study all of the traffic signs. However, automated vehicles are on the rise, and in the not-too-distant future, there will be no human drivers. In the Traffic Signs Recognition project, you’ll discover how software can use a picture as input to recognize the type of traffic sign. The German Traffic Signs Recognition Benchmark dataset (GTSRB) is used to train a Deep Neural Network that can identify the class of a traffic sign. A simple graphical user interface (GUI) to communicate with the application can also be created. Python can be used.
You must have heard about the self-driving cars in which the passenger can fully depend on the car for traveling. But to achieve level 5 autonomous, it is necessary for vehicles to understand and follow all traffic rules.
In the world of Artificial Intelligence and advancement in technologies, many researchers and big companies like Tesla, Uber, Google, Mercedes-Benz, Toyota, Ford, Audi, etc are working on autonomous vehicles and self-driving cars. So, for achieving accuracy in this technology, the vehicles should be able to interpret traffic signs and make decisions accordingly.
There are several different types of traffic signs like speed limits, no entry, traffic signals, turn left or right, children crossing, no passing of heavy vehicles, etc. Traffic signs classification is the process of identifying which class a traffic sign belongs to.
In this Python project example, we will build a deep neural network model that can classify traffic signs present in the image into different categories. With this model, we are able to read and understand traffic signs which are a very important task for all autonomous vehicles.
Traffic signs are an integral part of our road infrastructure. They provide critical information, sometimes compelling recommendations, for road users, which in turn requires them to adjust their driving behaviour to make sure they adhere with whatever road regulation currently enforced. Without such useful signs, we would most likely be faced with more accidents, as drivers would not be given critical feedback on how fast they could safely go, or informed about road works, sharp turn, or school crossings ahead. In our modern age, around 1.3M people die on roads each year. This number would be much higher without our road signs.
Naturally, autonomous vehicles must also abide by road legislation and therefore recognize and understand traffic signs.
Traditionally, standard computer vision methods were employed to detect and classify traffic signs, but these required considerable and time-consuming manual work to handcraft important features in images. Instead, by applying deep learning to this problem, we create a model that reliably classifies traffic signs, learning to identify the most appropriate features for this problem by itself. In this post, I show how we can create a deep learning architecture that can identify traffic signs with close to 98% accuracy on the test set.
The dataset is plit into training, test and validation sets, with the following characteristics:
Moreover, we will be using Python 3.5 with Tensorflow to write our code.
You can see below a sample of the images from the dataset, with labels displayed above the row of corresponding images. Some of them are quite dark so we will look to improve contrast a bit later.
There is also a significant imbalance across classes in the training set, as shown in the histogram below. Some classes have less than 200 images, while others have over 2000. This means that our model could be biased towards over-represented classes, especially when it is unsure in its predictions. We will see later how we can mitigate this discrepancy using data augmentation.