Traffic signs are an integral part of our road infrastructure. Traffic signs are road facilites that convey, guide, restrict, warn or instruct information using words or symbols. Traffic sign recognition plays an important role in expert systems such as traffic assistance driving systems and automatic driving systems.
You all must have heard about the self-driving cars in which the passengers can fully depend on the car for travelling. But to achieve level 5 autonomous, it is necessary for vehicles to understand and follow traffic rules. With the development of automative intelligent technology famous car companies, such as BMW, Mercedes-Benz, Audi are investing on ADAS(Advanced Driver Assistance System) research. ADAS includes TRS(Traffic Recognition System) along with some other advanced traffic assistance systems.
Traffic sign recognition is a process of classifying a traffic sign into different categories. Traffic-sign recognition (TSR) is a technology by which a vehicle is able to recognize the traffic signs put on the road e.g. "speed limit" or "children" or "turn ahead". This is part of the features collectively called ADAS. The technology is being developed by a variety of automotive suppliers. It uses image processing techniques to detect the traffic signs. The detection methods can be generally divided into color based, shape based and learning based methods.
The Dataset contains about 39210 image links of different traffic signs in Train.csv. The given images can be classified into 43 different classes. The given Dataset is little bit imbalanced some of the classes have many images while some classes have few images. The size of the dataset is 300MB. The dataset has a train folder which contains images inside each class and a test folder for testing the developed model.
There are 43 Different categories that are present in the dataset like no entry, speed limit, children crossing, etc. This web app can detect traffic signs in the uploaded image and can classify into different categories.
Speed limit (20km/h) Speed limit (30km/h) Speed limit (50km/h) Speed limit (60km/h) Speed limit (70km/h) Speed limit (80km/h) End of Speed limit (80km/h) Speed limit (100km/h) Speed limit (100km/h) Speed limit (120km/h) No Passing No Passing veh over 3.5 tons Right-of-way at intersection Priority Road Yield Stop No Vehicles Veh > 3.5 tons Prohibited No entry General Caution Dangerous Curve Left Dangerous Curve Right Double Curve Bumpy Road Slippery Road Road Narrows On The Right Road Work Traffic Signals Pedestrians Children Crossing Bicycles Crossing Beware Of ice/Snow Wild Animals Crossing End Speed + Passing Limits Turn Right Ahead Turn Left Ahead Ahead Only Go Straight Or Right Go Straight or Left Keep Right Keep Left Roundabout mandatory End Of No Passing End No Passing Veh > 3.5 tons Dataset link :Traffic Signs Dataset Kaggle
A CNN(Convolutional Neural Network) model is trained on the dataset, as CNN is best suited for image classification related tasks.
Following is the architecture of the CNN model :
2 Conv2D layers with filter = 32, kernel size = (5,5) and activation function as relu. Maxpool2D layer with pool size = (2,2) A Dropout layer with rate = 0.25 2 Conv2D layers with filter = 64, kernel size = (3,3) and activation function as relu. Maxpool2D layer with pool size = (2,2) A Dropout layer with rate = 0.25 Flatten layer to squeeze the layers into one dimension. A Dense fully connected layer with 256 nodes and relu as activation function. Dropout layer with rate as 0.5 At last a Dense layer with 43 nodes and Sigmoid as activation function.