Traffic Sign Recognition System using CNN
A traffic sign recognition system using CNN is a project that uses computer vision and deep learning techniques to automatically recognize and classify traffic signs from images or video streams. The system can be used in self-driving cars or advanced driver-assistance systems (ADAS) to improve road safety by assisting drivers in recognizing and responding to traffic signs.
Here is a high-level overview of the project:
Data Collection: Collect a large dataset of traffic sign images from different sources. This can include images of different types of traffic signs taken from different angles and under different lighting conditions.
Data Preprocessing: Clean the data and extract relevant features from the images. This can include techniques such as image normalization and resizing.
CNN Model Architecture: Design and train a CNN model for traffic sign recognition. This can involve selecting an appropriate CNN architecture such as VGG or ResNet, and fine-tuning the model for the specific task of traffic sign recognition.
Training Set and Test Set Split: Split the dataset into training and test sets. The training set is used to train the CNN model, while the test set is used to evaluate the performance of the trained model.
Model Evaluation: Evaluate the performance of the CNN model using performance metrics such as accuracy, precision, recall, and F1-score.
Deployment: Deploy the system in self-driving cars or ADAS to assist drivers in recognizing and responding to traffic signs.
Some possible enhancements to the system could include integrating it with other sensors such as LiDAR or radar to provide a more comprehensive view of the road environment. Additionally, the system could be expanded to include real-time detection and tracking of traffic signs in video streams.
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