Segmentation d’images urbaines par Deep Learning

Abstract

Semantic segmentation is a major area of computer vision that aims to assign a precise label to every pixel of an image, thereby enabling a deep and comprehensive understanding of its visual content. Since the rise of deep learning, this field has witnessed significant advances with the introduction of Convolutional Neural Networks (CNNs), which have demonstrated remarkable efficiency in extracting and representing visual features. Among the most popular models is U‐Net, initially designed for precise segmentation in medical contexts, which we have adopted as the foundation of this work. Structural modifications and additional dedicated layers have been introduced to enhance its ability to segment dense visual environments, such as the urban scenes found in the Cityscapes dataset, renowned for its richness and precise annotations. These improvements have significantly increased the model’s precision, while making the learning process more stable, faster, and better suited to the diversity of modern urban environments. This work thus provides a solid methodological basis for designing models adapted to the precise segmentation of complex visual environments.