Impact of data smoothing on semantic segmentation

Nuhman Ul Haq, Zia ur Rehman, Ahmad Khan*, Ahmad Din, Sajid Shah, Abrar Ullah, Fawad Qayum

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Semantic segmentation is the process to classify each pixel of an image. The current state-of-the-art semantic segmentation techniques use end-to-end trainable deep models. Generally, the training of these models is controlled by some external hyper-parameters rather to use the variation in data. In this paper, we investigate the impact of data smoothing on the training and generalization of deep semantic segmentation models. A mechanism is proposed to select the best level of smoothing to get better generalization of the deep semantic segmentation models. Furthermore, a smoothing layer is included in the deep semantic segmentation models to automatically adjust the level of smoothing. Extensive experiments are performed to validate the effectiveness of the proposed smoothing strategies.

Original languageEnglish
JournalNeural Computing and Applications
Early online date14 Sep 2020
Publication statusE-pub ahead of print - 14 Sep 2020


  • Deep learning
  • SegNet
  • Semantic segmentation
  • Smoothing

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


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