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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Inter-Level Feature Balanced Fusion Network for Street Scene Segmentation.

Dongqian Li1, Cien Fan1, Lian Zou1

  • 1School of Electronic Information, Wuhan University, Wuhan 430072, China.

Sensors (Basel, Switzerland)
|December 10, 2021
PubMed
Summary

This study introduces the Inter-Level Feature Balanced Fusion Network (IFBFNet) for semantic segmentation. IFBFNet enhances feature fusion, improving boundary recognition and overall performance on datasets like Cityscapes.

Keywords:
Cityscapesencoder–decoderfeature balanced fusionsemantic segmentation

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Area of Science:

  • Computer Vision
  • Deep Learning
  • Image Segmentation

Background:

  • Semantic segmentation is crucial for pixel-level recognition in various applications.
  • Current methods struggle with unbalanced fusion and low utilization of inter-level features due to simple concatenation or element-wise addition.

Purpose of the Study:

  • To propose the Inter-Level Feature Balanced Fusion Network (IFBFNet) for more balanced and effective inter-level feature fusion.
  • To improve the performance of semantic segmentation networks, particularly in boundary detail restoration.

Main Methods:

  • An encoder-decoder architecture is employed, utilizing a deep convolutional network in the encoder for semantic information extraction.
  • Skip-connections fuse low-level spatial features in the decoder, enhanced by an inter-level feature balanced fusion module.
  • A shallower spatial information stream is added to capture finer boundary details.

Main Results:

  • The proposed IFBFNet module effectively addresses issues of unbalanced feature fusion.
  • The network demonstrates competitive performance on the Cityscapes dataset, utilizing only finely annotated training data.
  • Significant improvements were observed compared to the baseline network.

Conclusions:

  • The Inter-Level Feature Balanced Fusion Network (IFBFNet) offers a more effective approach to inter-level feature fusion in semantic segmentation.
  • The method successfully enhances boundary representation and achieves state-of-the-art results on challenging datasets.