Traffic Sign Classification with Keras and Deep Learning
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.
We will train and validate a model so it can classify traffic sign images using the German Traffic Sign Dataset. https://www.kaggle.com/datasets/meowmeowmeowmeowmeow/gtsrb-german-traffic-sign
After the model is trained, you will then try out your model on images of German traffic signs on test set or any signs you want
In the real-world, traffic sign recognition is a two-stage process:
- Localization: Detect and localize where in an input image/frame a traffic sign is.
- Recognition: Take the localized ROI and actually recognize and classify the traffic sign.
We have included a Traffic Sign Architecture that contains further Convolution Neural Network:
The project begins with an introduction to the importance of traffic sign recognition and its significance in enhancing road safety. It highlights the challenges involved in this task, such as variations in lighting conditions, weather, and the presence of occlusions. The use of deep learning and CNNs is justified as an effective solution to overcome these challenges and achieve high classification accuracy.
The guide then proceeds to provide a detailed explanation of the underlying concepts of deep learning, including neural networks, CNNs, and their architectural components such as convolutional layers, pooling layers, and fully connected layers. It also covers essential topics like activation functions, loss functions, and optimization algorithms that play a vital role in training deep learning models.
Next, the project focuses on data preprocessing and augmentation techniques. It explains how to acquire and preprocess traffic sign datasets, ensuring that the images are properly normalized, resized, and prepared for training. Data augmentation techniques, such as random rotations, translations, and flips, are explored to increase the robustness and generalization ability of the model.
The core of the project involves constructing and training a CNN model using Keras. The architecture of the CNN is carefully designed, considering the specific requirements of traffic sign classification. The guide illustrates how to configure and compile the model, select appropriate hyperparameters, and implement techniques such as regularization and dropout to avoid overfitting.
To evaluate the performance of the trained model, the project covers techniques for splitting the dataset into training and testing sets, as well as strategies for cross-validation and performance metrics such as accuracy, precision, recall, and F1 score. It also discusses strategies for interpreting the model's predictions and visualizing the CNN's learned features.
Lastly, the guide explores potential avenues for improving the model's performance, including the use of transfer learning and fine-tuning pre-trained models, as well as exploring more advanced techniques like object detection and localization for traffic sign recognition.
By the end of the project, readers will have a solid understanding of how to apply deep learning techniques using Keras to tackle traffic sign classification problems. They will be equipped with the knowledge and practical skills necessary to build robust models capable of accurately identifying and classifying traffic signs, contributing to