Overview / Usage

Traffic sign classification is the process of automatically recognizing traffic signs along the road, including speed limit signs, yield signs, merge signs, etc. Being able to automatically recognize traffic signs enables us to build “smarter cars”.

Self-driving cars need traffic sign recognition in order to properly parse and understand the roadway. Similarly, “driver alert” systems inside cars need to understand the roadway around them to help aid and protect drivers.

Traffic sign recognition is just one of the problems that computer vision and deep learning can solve.

The dataset we’ll be using to train our own custom traffic sign classifier is the German Traffic Sign Recognition Benchmark (GTSRB).

The GTSRB dataset consists of 43 traffic sign classes and nearly 50,000 images.

Methodology / Approach

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1. Localization: Detect and localize where in an input image/frame a traffic sign is.
2. Recognition: Take the localized ROI and actually recognize and classify the traffic sign.

Deep learning object detectors can perform localization and recognition in a single forward-pass of the network — if you’re interested in learning more about object detection and traffic sign localization using Faster R-CNNs, Single Shot Detectors (SSDs), and RetinaNet, be sure to refer to my book, Deep Learning for Computer Vision with Python, where I cover the topic in detail.

Technologies Used

1. TensorFlow

2. Keras

3. Numpy

4. OpenCV

5. Matplotlib

Repository

https://github.com/TausifAnsari/Traffic-Sign-Recognizer