Automated Image Segmentation for Complex Scenes Using U-Net Architecture

Image segmentation is a foundational task in computer vision, enabling systems to interpret and analyze visual data by partitioning images into meaningful regions. This study presents an automated approach to image segmentation in complex scenes using the U-Net architecture. Originally developed for biomedical image analysis, U-Net has demonstrated impressive performance in diverse segmentation tasks due to its encoder-decoder structure with skip connections. The proposed model is trained on a dataset of complex real-world scenes containing multiple overlapping objects, varying lighting conditions, and background clutter. The results indicate that U-Net can effectively capture spatial hierarchies and preserve fine details, achieving high segmentation accuracy even in challenging scenarios. This research highlights the adaptability of U-Net for real-time applications in domains such as autonomous navigation, surveillance, and remote sensing.