toldodm/traffic-sign-recognition

Traffic Sign Recognition: a performance comparison on challenging weather conditions between CNNs with spatial transformers and ResNet50

Overview

The task is the classification of a traffic sign among 43 possible classes over real images of traffic signs captured in Germany.

The classification has been done implementing the state-of-the-art models for this task (according to GTSRB Benchamark) and exploring the potentiality of ResNet50 as backbone for transfer learning.

The comparison between the models is presented in our report.

Features

• Traffic Sign Recognition: Classify the traffic signs from images.
• Convolutational Neural Networks: Utilize convolutional neural networks for feature extraction.
• Convolutional Neural Network with Spatial Transformers: Incorporate spatial transformers to handle geometric transformations and improve accuracy over CNN architecture.
• Transfer learning using ResNet50: Training of the last fully connected layers of the backbone keeping the weights for all the other layers locked.

Dataset

• GTSRB Dataset: German Traffic Sign Recognition Benchmark (GTSRB)
• Description:
  • 43 classes of traffic signs
  • 51840 images of different sizes
• Weather augmented datasets (Weather50-Weather100): Adding weather augmentations images to GTSRB dataset with a respectively fixed probability of the augmentation to be applied of 0.5 and 1.

Models

• Models can be found here: https://drive.google.com/drive/folders/1P0Vv7XsjSRJ90Za2QqmW0gQHPsd9ucO1?usp=drive_link

Classification example of CNN with spatial transformers on a weather augmented dataset

Classification examples of a convolutional neural network with spatial transformers on weather aug- mented dataset.

The effects starting from the left are: rain, shadow, sun flare, fog. On the top row the predicted label with its confi- dence, while on the second row the ground truth class.

Requirements

• Python 3.11
• PyTorch 2.3.0
• Other libraries listed in requirements.txt