The objective of this project is to leverage deep learning and computer vision techniques to accurately identify traffic signs in Australia. This capability is crucial for the development of autonomous vehicles that can safely navigate Australian roads and streets in the future.
Background: Autonomous vehicles have made significant strides over the past decade and are expected to become a part of daily life soon. A key challenge these vehicles face is the accurate recognition of road signs. In this project, you'll train a deep learning model to effectively classify a variety of road signs.
For reference, a list of road signs used in Victoria can be found here: https://www.vicroads.vic.gov.au/safety-and-road-rules/road-rules/a-to-z-of-road-rules/road-signs.
Given the large number of road signs, achieving high accuracy across all sign types can be challenging. To simplify the task, you can focus on a specific sub-problem, such as classifying critical signs like stop signs, speed limits, and school zones, or categorizing signs more broadly (e.g., regulatory, warning, or freeway signs).
Part 1: Problem Definition and Objective
Clarifying the Task: The project focuses on classifying traffic signs, with the option to specialize in particular categories such as critical regulatory signs (e.g., Stop signs, Speed limits, School zones) or explore a broader classification of regulatory, warning, and freeway signs.
Key Challenges: Highlight both theoretical and practical challenges, such as differentiating between similar-looking signs (e.g., varying speed limits), handling lighting and visibility issues, and dealing with occlusions. Additionally, managing data collection, labeling accuracy, and computational limitations will need attention.
Inputs and Outputs: The input will consist of images of road signs, while the output will be the predicted classification of the sign, such as “Stop Sign” or “Warning Sign.”
Deliverable: A clear document outlining the problem, challenges, and desired outcomes.
Part 2: Dataset Development and Preparation
Dataset Selection: Identify a relevant dataset of Australian road signs. You may choose an existing dataset like GTSRB, with necessary modifications for Australian conditions, or create your own dataset using available images (e.g., from VicRoads).
Data Partitioning: Properly split the data into training, validation, and testing subsets, ensuring each class is equally represented.
Labeling Process: If working with a custom dataset, ensure all images are accurately labeled, using tools like LabelImg for assistance.
Deliverable: A well-organized dataset of at least 1,000 images, labeled and divided appropriately across training, validation, and testing sets.
Part 3: Model Selection and Training
Choosing a Model: Select an appropriate deep learning model, such as a Convolutional Neural Network (CNN) or a pre-trained architecture like ResNet50 or VGG16, depending on the dataset size.
Model Implementation: Build the model using TensorFlow or PyTorch, paying close attention to key parameters like the learning rate, optimizer, and loss function.
Initial Training: Train the model on the training data and evaluate its performance on the validation set, reporting key metrics like accuracy, precision, recall, and F1-score.
Deliverable: A trained model with initial performance metrics.
Part 4: Data Augmentation and Optimization
Augmentation Techniques: Create a data augmentation pipeline, using transformations like random rotation, flipping, zooming, or adding noise. You can use existing libraries like Keras’ ImageDataGenerator or implement custom augmentations.
Performance Evaluation: Train the model with and without augmented data, and compare both the model’s performance and the training time between the two scenarios.
Pipeline Optimization: Use tools like TensorFlow Profiler to analyze the input pipeline’s efficiency and optimize where necessary.
Deliverable: A detailed comparison of the model’s performance and training times with and without data augmentation, as well as an optimized input pipeline.
Part 5: Generalization and Error Analysis
Misclassification Review: Conduct a thorough review of misclassified images to identify common patterns, such as low contrast, occlusions, or challenging visual conditions.
Domain Generalization: Test the model’s ability to generalize by introducing domain variations, such as images from different lighting conditions or those taken with different devices. Measure how well the model handles these new inputs.
Performance Comparison: Compare the model’s performance across the original test data and the newly introduced domain-shifted data.
Deliverable: An analysis of the model’s ability to generalize across different domains, with examples of misclassified cases and areas for improvement.
Part 6: Improving Generalization via Penalizing Gradient Norm
Research and Implementation: Study the concept of penalizing gradient norm as a strategy to improve generalization in deep learning, and integrate this method into your model.
Application to Traffic Signs: Compare the model’s performance before and after applying this technique, particularly on data that poses generalization challenges.
Reproduction of Results: Reproduce experiments from existing research to compare outcomes, identifying any differences and providing insights into the model’s behavior.
Deliverable: A refined model that demonstrates improved generalization, along with an analysis of the effectiveness of gradient norm penalization.
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