Résumé:
Real-time semantic segmentation is a key task in computer vision, especially for
autonomous driving systems where quick and accurate perception is essential. This thesis
explores deep learning techniques for this task using the U-Net architecture, applied to the
Cityscapes dataset of urban driving scenes.
To enhance performance, we replaced the original U-Net encoder with
EfficientNet-B0, aiming to achieve both high segmentation accuracy and real-time
inference speed. The proposed model reached over 61.8% mean IoU and 128 FPS, making
it highly suitable for real-time applications.
The work was developed using Google Colab and benefited from an internship at
CRTI, which provided technical support and valuable resources. Despite certain challenges
such as limited annotation in the test set, the final results demonstrate the model’s
efficiency and robustness.
