Leveraging Deep Learning Techniques to Obtain Efficacious Segmentation Results

Abstract

Image segmentation is a critical task in the field of computer vision. In the past, traditional segmentation algorithms were frequently used to tackle this problem but had various shortcomings. However, the advent of deep learning has revolutionized this field, leading to the development of novel image segmentation algorithms. This paper presents a comprehensive overview of deep learning-based models applied to medical imaging, iris recognition, pedestrian detection, and autonomous driving. The study encompasses various techniques, such as convolutional neural networks, fully convolutional neural networks, encoder–decoder architectures, multi-scale approaches, attention mechanisms, and image transformers. Moreover, this paper evaluates the performance of these models on relevant datasets, providing insightful recommendations for researchers to integrate promising techniques into their work for specific applications. The discussion also explores the challenges, constraints, and potential research directions in these domains.