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<p>Semantic segmentation plays an important role in understanding the visual content of images by assigning a specific label to each individual pixel. Recently, deep learning approaches have emerged and surpassed the benchmark for the semantic segmentation problem. This paper provides a comprehensive survey of these techniques, categorizing them into nine distinct types based on their primary contributions. Beyond methods, we highlight the real-world applicability of semantic segmentation by extensively reviewing its applications in critical domains, including medical image analysis, autonomous vehicles, and remote sensing. This dual focus on methods and applications offers a well-rounded perspective, bridging theoretical advancements and practical implementations.</p>

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A Comprehensive Investigation into Semantic Segmentation and its Applications

  • 期刊论文

  • 发表日期：

    2025年10月8日

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  • Khanh Hung Vu,
  • Duc Phuc Nguyen,
  • Hoang-Anh Pham

摘要

Semantic segmentation plays an important role in understanding the visual content of images by assigning a specific label to each individual pixel. Recently, deep learning approaches have emerged and surpassed the benchmark for the semantic segmentation problem. This paper provides a comprehensive survey of these techniques, categorizing them into nine distinct types based on their primary contributions. Beyond methods, we highlight the real-world applicability of semantic segmentation by extensively reviewing its applications in critical domains, including medical image analysis, autonomous vehicles, and remote sensing. This dual focus on methods and applications offers a well-rounded perspective, bridging theoretical advancements and practical implementations.