Enhancing Semantic Segmentation: Design and Analysis of Improved U-Net Based Deep Convolutional Neural Networks

Abstract

In this research, we provide a state-of-the-art method for semantic segmentation that makes use of a modified version of the U-Net architecture, which is itself based on deep convolutional neural networks (CNNs). This research delves into the ins and outs of this cutting-edge approach to semantic segmentation in an effort to boost its precision and productivity. To perform semantic segmentation, a crucial operation in computer vision, each pixel in an image must be assigned to one of many predefined item classes. The proposed Improved U-Net architecture makes use of deep CNNs to efficiently capture complex spatial characteristics while preserving associated context. The study illustrates the efficacy of the Improved U-Net in a variety of real-world circumstances through thorough experimentation and assessment. Intricate feature extraction, down-sampling, and up-sampling are all part of the network's design in order to produce high-quality segmentation results. The study demonstrates comparative evaluations against classic U-Net and other state-of-the-art models and emphasizes the significance of hyperparameter fine-tuning. The suggested architecture shows excellent performance in terms of accuracy and generalization, demonstrating its promise for a variety of applications. Finally, the problem of semantic segmentation is addressed in a novel way. The experimental findings validate the relevance of the architecture's design decisions and demonstrate its potential to boost computer vision by enhancing segmentation precision and efficiency.