Real-Time Semantic Segmentation Algorithms for Enhanced Augmented Reality

Abstract

Semantic segmentation plays a pivotal role in a wide range of real-time applications, including autonomous driving, medical imaging, robotics, and augmented reality, where precise pixel-level understanding of scenes is essential. This paper offers a comprehensive overview of semantic segmentation techniques, highlighting their evolution and significance in real-time systems. We explore the fundamental principles and methodologies underlying these techniques, examining both classical and state-of-the-art approaches. The discussion includes an evaluation of the performance of various models, with a focus on their accuracy, speed, and resource efficiency, which are critical for real-time deployment. Furthermore, we address the practical challenges associated with implementing semantic segmentation in real-world scenarios, such as limited computational power, latency constraints, and scalability. Recent advancements in hardware acceleration, algorithm optimization, and lightweight model design are also reviewed, offering insights into future directions for enhancing real-time semantic segmentation in diverse domains.