Introduction Traffic sign recognition is an essential component of autonomous driving systems and advanced driver assistance systems (ADAS). This project addresses the challenge of accurately classifying traffic signs from images captured by onboard cameras.
Dataset The custom dataset used in this project consists of images of various traffic signs collected from real-world scenarios. The dataset is annotated with corresponding labels indicating the type of traffic sign in each image. It is divided into training, validation, and test sets to facilitate model training, validation, and evaluation.
Model Architecture The deep learning model employed in this project utilizes a convolutional neural network (CNN) architecture. CNNs have demonstrated superior performance in image classification tasks due to their ability to automatically learn hierarchical features from input images. The model architecture is designed to effectively capture and leverage spatial hierarchies present in traffic sign images.
Training The model is trained using the training set of annotated traffic sign images. During training, the model learns to map input images to their corresponding traffic sign labels by minimizing a predefined loss function using stochastic gradient descent (SGD) optimization. The training process involves iteratively adjusting the model's parameters to minimize prediction errors.
Evaluation The trained model's performance is evaluated using the separate test set of traffic sign images. Classification accuracy, precision, recall, and F1 score are computed to assess the model's ability to correctly classify traffic signs. Additionally, qualitative analysis, such as visualizing model predictions on sample images, provides insights into the model's behavior and potential areas for improvement.
Usage To use the trained model for traffic sign classification:
Install the required dependencies listed in requirements.txt. Load the pre-trained model weights. Preprocess input images to meet the model's input requirements. Feed the preprocessed images into the model for inference. Obtain predicted traffic sign labels from the model's output. Refer to the provided documentation and example scripts for detailed instructions on using the model for traffic sign classification.
Contributing Contributions to this project are welcome! If you have ideas for improvements or new features, feel free to open an issue or submit a pull request. Please follow the project's code of conduct and contribution guidelines.