Enhanced Image Segmentation Using Deep Learning: A Comparative Study

Abstract

Image segmentation is a crucial task in computer vision, with applications spanning from medical imaging to autonomous driving. This study presents a comprehensive comparative analysis of three prominent deep learning models: U-Net, Fully Convolutional Networks (FCN), and SegNet. These models were evaluated across multiple datasets, including ISBI 2012, Cityscapes, and Pascal VOC, to assess their performance in terms of accuracy, efficiency, and generalization. The results indicate that U-Net excels in tasks requiring high precision, particularly in medical imaging, due to its ability to capture intricate details through its encoder-decoder architecture with skip connections. FCN demonstrates a balanced performance, making it suitable for urban scene segmentation, while SegNet stands out for its computational efficiency, making it ideal for real-time applications such as autonomous driving. However, the study also highlights potential limitations, including data biases and the need for more diverse datasets to improve model generalization. These findings contribute to the ongoing development of deep learning-based image segmentation and provide practical guidance for selecting appropriate models based on specific application needs.