Semantic Segmentation Techniques - Applications and Challenges: Investigating semantic segmentation techniques for pixel-level labeling of objects and scenes in images and videos

Abstract

 Semantic segmentation is a fundamental task in computer vision that involves labeling each pixel in an image with a corresponding class label. This paper provides a comprehensive review of semantic segmentation techniques, focusing on their applications and challenges. We first introduce the concept of semantic segmentation and its importance in various fields such as autonomous driving, medical imaging, and video surveillance. We then discuss the evolution of semantic segmentation algorithms from classical methods to deep learning-based approaches.

Next, we categorize semantic segmentation techniques based on their underlying architecture, including fully convolutional networks (FCNs), encoder-decoder networks, and pyramid scene parsing networks (PSPNet). For each category, we analyze the strengths and weaknesses of different approaches and highlight their applications in real-world scenarios.

Furthermore, we delve into the challenges faced by semantic segmentation algorithms, such as handling occlusions, dealing with small object instances, and ensuring real-time performance. We also discuss recent advancements in semantic segmentation, such as the integration of attention mechanisms and the use of generative adversarial networks (GANs) for data augmentation.

Finally, we outline future research directions in semantic segmentation, including the development of more efficient algorithms for resource-constrained environments and the exploration of multi-modal approaches for improved scene understanding.