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A Hierarchical Feature Extraction Network for Fast Scene Segmentation.

Liu Miao1, Yi Zhang1

  • 1National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu 610017, China.

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|November 27, 2021
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Summary

This study introduces the Hierarchical Feature Extraction Network (HFEN), a computer vision model that balances inference speed and semantic segmentation accuracy. HFEN achieves superior performance on benchmark datasets, offering real-time segmentation capabilities.

Keywords:
hierarchical feature extractionscene understandingsemantic segmentation

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Area of Science:

  • Computer Vision
  • Deep Learning
  • Image Segmentation

Background:

  • Semantic segmentation is crucial for assigning labels to image pixels.
  • Existing methods often struggle to balance speed and accuracy.

Purpose of the Study:

  • Introduce HFEN, a lightweight network for efficient semantic segmentation.
  • Achieve a balance between inference speed and segmentation accuracy.

Main Methods:

  • Utilize an encoder-decoder framework for feature extraction and fusion.
  • Employ down-sampling in the encoder for multi-layer feature extraction.
  • Aggregate global contextual and spatial information in the decoder.

Main Results:

  • HFEN demonstrates superior performance on Cityscapes and Camvid benchmarks.
  • The network achieves real-time performance for semantic segmentation.
  • Superior results were observed on NVIDIA 2080Ti hardware.

Conclusions:

  • HFEN offers an effective solution for real-time semantic segmentation.
  • The proposed architecture successfully balances speed and accuracy.
  • HFEN shows promise for practical computer vision applications.