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Low-Light Image Segmentation on Edge Computing System.

Sung-Chan Choi1,2, Sung-Yeon Kim3

  • 1Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea.

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|January 10, 2026
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Summary
This summary is machine-generated.

This study presents a novel three-step algorithm for segmenting low-light images, combining brightness enhancement with U-Net segmentation. The method achieves real-time processing speeds on edge devices.

Keywords:
deep learningedge computingimage segmentationlow-light image

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Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Low-light image segmentation is difficult due to poor visibility.
  • Existing methods often struggle with accuracy and speed.
  • Tunnel crack detection requires robust low-light image analysis.

Purpose of the Study:

  • To develop an effective algorithm for segmenting low-light images.
  • To enhance image brightness and contrast before segmentation.
  • To achieve accurate and real-time segmentation on edge platforms.

Main Methods:

  • A three-step algorithm combining image enhancement and segmentation.
  • Utilizing deep learning for low-light image enhancement.
  • Employing a U-Net model for pixel-level segmentation.
  • Implementing the pipeline on an edge computing platform.

Main Results:

  • The proposed algorithm significantly improves low-light image segmentation performance.
  • Experimental results demonstrate superior performance compared to baseline models.
  • The algorithm achieves sufficient speed for real-time processing on edge devices.

Conclusions:

  • The combined approach of enhancement and U-Net segmentation is effective for low-light images.
  • The algorithm is suitable for real-time applications like tunnel crack detection.
  • Edge computing implementation validates the practical utility of the proposed method.