Traffic sign detection is a high relevance computer vision problem and is the basis for a lot of applications in industry such as Automotive etc. Traffic signs can provide a wide range of variations between classes in terms of color, shape, and the presence of pictograms or text. In this project, I implemented a deep learning algorithm that will train on German traffic sign images and then check accuracy of the classification and output it. The deep learning model will be built using TensorFlow and we will also understand various ways to pre-process images using OpenCV.
The German Traffic Sign Recognition Benchmark (GTSRB), which was presented at the 2011 International Joint Conference on Neural Networks (IJCNN) has been used. The internal traffic signs are collected from the real road traffic environment in Germany, and it has become a common traffic sign dataset used by experts and scholars in computer vision, self-driving and other fields. The GTSRB comprises 51,839 images, which are divided into training and testing sets. A total of 39,209 and 12,630 images are provided in the training and testing sets, accounting for approximately 75% and 25% of the whole, respectively. Each image contains only one traffic sign, which is not necessarily located in the center of the image. The image size is unequal; the maximum and smallest images are 250 _ 250 and 15 _ 15 pixels, respectively. Dataset Link :- https://www.kaggle.com/datasets/meowmeowmeowmeowmeow/gtsrb-german-traffic-sign