Semantic Segmentation Using Deep Learning

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@article{1709374,
      author = {M. Sai Akash, Dr. S. Praveena},
      title = {Semantic Segmentation Using Deep Learning},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {12},
      pages = {1362-1367},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709374.pdf},
      abstract = {Semantic image Segmentation is an emerging task in the field of automation. Its application varies from autonomous driving to medical diagnosis. Semantic segmentation of an image means to label each pixel in that image to a particular class. As an example consider an outdoor street image where there are different objects like car, road, sky, trees, pedestrians etc. After applying semantic segmentation each pixel in the image belonging to the car will have the label car and road will have label road and so on. A recent trend in performing semantic segmentation is by using Convolutional Neural Networks, (CNN), which acted as a catalyst for segmentation. In this paper, a detailed discussion of various approaches for segmentation using CNN has been presented. Also, various datasets and their format and evaluations metrics are discussed. All the approaches discussed are diverse and has its pros and cons. Finally, an application-specific semantic segmentation method using a Deeplabv3+ algorithm for classification task has been used. The used method has shown improvement in the Miou score when tested on the CamVid dataset.},
      keywords = {CNN, Segmentation, FCN, Convolution, DeepLab .},
      month = {June},
  }